Your Modern Business Guide To Data Analysis Methods And Techniques

Data analysis methods and techniques blog post by datapine

Table of Contents

1) What Is Data Analysis?

2) Why Is Data Analysis Important?

3) What Is The Data Analysis Process?

4) Types Of Data Analysis Methods

5) Top Data Analysis Techniques To Apply

6) Quality Criteria For Data Analysis

7) Data Analysis Limitations & Barriers

8) Data Analysis Skills

9) Data Analysis In The Big Data Environment

In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.

Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.

With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.

In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis. 

To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.

What Is Data Analysis?

Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.

All these various methods are largely based on two core areas: quantitative and qualitative research.

To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:

Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.

Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include: 

  • Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate. 
  • Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes. 
  • Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic. 
  • Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.

Why Is Data Analysis Important?

Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.

  • Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
  • Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply. 
  • Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.

What Is The Data Analysis Process?

Data analysis process graphic

When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis. 

  • Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step. 
  • Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others.  An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario. 
  • Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data. 
  • Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others. 
  • Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them. 

Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.

17 Essential Types Of Data Analysis Methods

Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.

a) Descriptive analysis - What happened.

The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.

Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.

b) Exploratory analysis - How to explore data relationships.

As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of ​​application for it is data mining.

c) Diagnostic analysis - Why it happened.

Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.

Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.

c) Predictive analysis - What will happen.

The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.

With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.

e) Prescriptive analysis - How will it happen.

Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.

By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.

Top 17 data analysis methods

As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches. 

Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world: 

A. Quantitative Methods 

To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods. 

1. Cluster analysis

The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.

Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.

2. Cohort analysis

This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.

Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.  

A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.

Cohort analysis chart example from google analytics

3. Regression analysis

Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.

Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.

If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.

4. Neural networks

The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.

A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist. 

Here is an example of how you can use the predictive analysis tool from datapine:

Example on how to use predictive analytics tool from datapine

**click to enlarge**

5. Factor analysis

The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.

A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.

If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.

6. Data mining

A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge.  When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.

An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.

In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.

Example on how to use intelligent alerts from datapine

7. Time series analysis

As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result. 

In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events. 

A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.  

8. Decision Trees 

The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.

But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision. 

Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely.  Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision.  In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.

9. Conjoint analysis 

Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more. 

A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments. 

10. Correspondence Analysis

Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic. 

This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example. 

Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of. 

11. Multidimensional Scaling (MDS)

MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all”  and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses.  When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all. 

Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading. 

Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best. 

A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.

Example of multidimensional scaling analysis

Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data. 

B. Qualitative Methods

Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.

12. Text analysis

Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.

Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .

By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next. 

13. Content Analysis

This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.

There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context. 

Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question. 

14. Thematic Analysis

Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service. 

Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore,  to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize. 

15. Narrative Analysis 

A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others. 

From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.  

The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study. 

16. Discourse Analysis

Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on. 

From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice. 

17. Grounded Theory Analysis

Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data. 

All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes. 

How To Analyze Data? Top 17 Data Analysis Techniques To Apply

17 top data analysis techniques by datapine

Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.

1. Collaborate your needs

Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.

2. Establish your questions

Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.

To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .

3. Data democratization

After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.

Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.  

Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.

data connectors from datapine

4. Think of governance 

When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical. 

To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole. 

5. Clean your data

After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.

There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.

Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors. 

Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.

6. Set your KPIs

Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.

KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.

To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .

Transportation costs logistics KPIs

7. Omit useless data

Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.

Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.

Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.

8. Build a data management roadmap

While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.

Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.

9. Integrate technology

There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.

Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.

By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.

For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .

10. Answer your questions

By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.

11. Visualize your data

Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.

The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .

An executive dashboard example showcasing high-level marketing KPIs such as cost per lead, MQL, SQL, and cost per customer.

This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.

In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .

The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.

12. Be careful with the interpretation

We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations. 

To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:

  • Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation. 
  • Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
  • Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.

13. Build a narrative

Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.

The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.

By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.

14. Consider autonomous technology

Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.

Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.

At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.

15. Share the load

If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.

Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.

Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.

16. Data analysis tools

In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.

  • Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
  • Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
  • SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
  • Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.

17. Refine your process constantly 

Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving. 

Quality Criteria For Data Analysis

So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in. 

  • Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low. 
  • External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high. 
  • Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now. 
  • Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps. 

The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource . 

Data Analysis Limitations & Barriers

Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail. 

  • Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions. 
  • Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective. 
  • Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them. 
  • Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them. 
  • Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.   
  • Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy. 
  • Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way. 
  • Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data. 

Key Data Analysis Skills

As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.

  • Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers. 
  • Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate. 
  • Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient. 
  • SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis. 
  • Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context. 

Data Analysis In The Big Data Environment

Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.

To inspire your efforts and put the importance of big data into context, here are some insights that you should know:

  • By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
  • 94% of enterprises say that analyzing data is important for their growth and digital transformation. 
  • Companies that exploit the full potential of their data can increase their operating margins by 60% .
  • We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.

Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.

Key Takeaways From Data Analysis 

As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.

17 Essential Types of Data Analysis Methods:

  • Cluster analysis
  • Cohort analysis
  • Regression analysis
  • Factor analysis
  • Neural Networks
  • Data Mining
  • Text analysis
  • Time series analysis
  • Decision trees
  • Conjoint analysis 
  • Correspondence Analysis
  • Multidimensional Scaling 
  • Content analysis 
  • Thematic analysis
  • Narrative analysis 
  • Grounded theory analysis
  • Discourse analysis 

Top 17 Data Analysis Techniques:

  • Collaborate your needs
  • Establish your questions
  • Data democratization
  • Think of data governance 
  • Clean your data
  • Set your KPIs
  • Omit useless data
  • Build a data management roadmap
  • Integrate technology
  • Answer your questions
  • Visualize your data
  • Interpretation of data
  • Consider autonomous technology
  • Build a narrative
  • Share the load
  • Data Analysis tools
  • Refine your process constantly 

We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.

Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .

And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .

Table of Contents

What is data analysis, why is data analysis important, what is the data analysis process, data analysis methods, applications of data analysis, top data analysis techniques to analyze data, what is the importance of data analysis in research, future trends in data analysis, choose the right program, what is data analysis: a comprehensive guide.

What Is Data Analysis: A Comprehensive Guide

In the contemporary business landscape, gaining a competitive edge is imperative, given the challenges such as rapidly evolving markets, economic unpredictability, fluctuating political environments, capricious consumer sentiments, and even global health crises. These challenges have reduced the room for error in business operations. For companies striving not only to survive but also to thrive in this demanding environment, the key lies in embracing the concept of data analysis . This involves strategically accumulating valuable, actionable information, which is leveraged to enhance decision-making processes.

If you're interested in forging a career in data analysis and wish to discover the top data analysis courses in 2024, we invite you to explore our informative video. It will provide insights into the opportunities to develop your expertise in this crucial field.

Your Data Analytics Career is Around The Corner!

Your Data Analytics Career is Around The Corner!

Data analysis inspects, cleans, transforms, and models data to extract insights and support decision-making. As a data analyst , your role involves dissecting vast datasets, unearthing hidden patterns, and translating numbers into actionable information.

Data analysis plays a pivotal role in today's data-driven world. It helps organizations harness the power of data, enabling them to make decisions, optimize processes, and gain a competitive edge. By turning raw data into meaningful insights, data analysis empowers businesses to identify opportunities, mitigate risks, and enhance their overall performance.

1. Informed Decision-Making

Data analysis is the compass that guides decision-makers through a sea of information. It enables organizations to base their choices on concrete evidence rather than intuition or guesswork. In business, this means making decisions more likely to lead to success, whether choosing the right marketing strategy, optimizing supply chains, or launching new products. By analyzing data, decision-makers can assess various options' potential risks and rewards, leading to better choices.

2. Improved Understanding

Data analysis provides a deeper understanding of processes, behaviors, and trends. It allows organizations to gain insights into customer preferences, market dynamics, and operational efficiency .

3. Competitive Advantage

Organizations can identify opportunities and threats by analyzing market trends, consumer behavior , and competitor performance. They can pivot their strategies to respond effectively, staying one step ahead of the competition. This ability to adapt and innovate based on data insights can lead to a significant competitive advantage.

Join The Ranks of Top-Notch Data Analysts!

Join The Ranks of Top-Notch Data Analysts!

4. Risk Mitigation

Data analysis is a valuable tool for risk assessment and management. Organizations can assess potential issues and take preventive measures by analyzing historical data. For instance, data analysis detects fraudulent activities in the finance industry by identifying unusual transaction patterns. This not only helps minimize financial losses but also safeguards the reputation and trust of customers.

5. Efficient Resource Allocation

Data analysis helps organizations optimize resource allocation. Whether it's allocating budgets, human resources, or manufacturing capacities, data-driven insights can ensure that resources are utilized efficiently. For example, data analysis can help hospitals allocate staff and resources to the areas with the highest patient demand, ensuring that patient care remains efficient and effective.

6. Continuous Improvement

Data analysis is a catalyst for continuous improvement. It allows organizations to monitor performance metrics, track progress, and identify areas for enhancement. This iterative process of analyzing data, implementing changes, and analyzing again leads to ongoing refinement and excellence in processes and products.

The data analysis process is a structured sequence of steps that lead from raw data to actionable insights. Here are the answers to what is data analysis:

  • Data Collection: Gather relevant data from various sources, ensuring data quality and integrity.
  • Data Cleaning: Identify and rectify errors, missing values, and inconsistencies in the dataset. Clean data is crucial for accurate analysis.
  • Exploratory Data Analysis (EDA): Conduct preliminary analysis to understand the data's characteristics, distributions, and relationships. Visualization techniques are often used here.
  • Data Transformation: Prepare the data for analysis by encoding categorical variables, scaling features, and handling outliers, if necessary.
  • Model Building: Depending on the objectives, apply appropriate data analysis methods, such as regression, clustering, or deep learning.
  • Model Evaluation: Depending on the problem type, assess the models' performance using metrics like Mean Absolute Error, Root Mean Squared Error , or others.
  • Interpretation and Visualization: Translate the model's results into actionable insights. Visualizations, tables, and summary statistics help in conveying findings effectively.
  • Deployment: Implement the insights into real-world solutions or strategies, ensuring that the data-driven recommendations are implemented.

Become an Expert in Data Analytics!

Become an Expert in Data Analytics!

1. Regression Analysis

Regression analysis is a powerful method for understanding the relationship between a dependent and one or more independent variables. It is applied in economics, finance, and social sciences. By fitting a regression model, you can make predictions, analyze cause-and-effect relationships, and uncover trends within your data.

2. Statistical Analysis

Statistical analysis encompasses a broad range of techniques for summarizing and interpreting data. It involves descriptive statistics (mean, median, standard deviation), inferential statistics (hypothesis testing, confidence intervals), and multivariate analysis. Statistical methods help make inferences about populations from sample data, draw conclusions, and assess the significance of results.

3. Cohort Analysis

Cohort analysis focuses on understanding the behavior of specific groups or cohorts over time. It can reveal patterns, retention rates, and customer lifetime value, helping businesses tailor their strategies.

4. Content Analysis

It is a qualitative data analysis method used to study the content of textual, visual, or multimedia data. Social sciences, journalism, and marketing often employ it to analyze themes, sentiments, or patterns within documents or media. Content analysis can help researchers gain insights from large volumes of unstructured data.

5. Factor Analysis

Factor analysis is a technique for uncovering underlying latent factors that explain the variance in observed variables. It is commonly used in psychology and the social sciences to reduce the dimensionality of data and identify underlying constructs. Factor analysis can simplify complex datasets, making them easier to interpret and analyze.

6. Monte Carlo Method

This method is a simulation technique that uses random sampling to solve complex problems and make probabilistic predictions. Monte Carlo simulations allow analysts to model uncertainty and risk, making it a valuable tool for decision-making.

7. Text Analysis

Also known as text mining , this method involves extracting insights from textual data. It analyzes large volumes of text, such as social media posts, customer reviews, or documents. Text analysis can uncover sentiment, topics, and trends, enabling organizations to understand public opinion, customer feedback, and emerging issues.

8. Time Series Analysis

Time series analysis deals with data collected at regular intervals over time. It is essential for forecasting, trend analysis, and understanding temporal patterns. Time series methods include moving averages, exponential smoothing, and autoregressive integrated moving average (ARIMA) models. They are widely used in finance for stock price prediction, meteorology for weather forecasting, and economics for economic modeling.

Want to Become a Data Analyst? Learn From Experts!

Want to Become a Data Analyst? Learn From Experts!

9. Descriptive Analysis

Descriptive analysis   involves summarizing and describing the main features of a dataset. It focuses on organizing and presenting the data in a meaningful way, often using measures such as mean, median, mode, and standard deviation. It provides an overview of the data and helps identify patterns or trends.

10. Inferential Analysis

Inferential analysis   aims to make inferences or predictions about a larger population based on sample data. It involves applying statistical techniques such as hypothesis testing, confidence intervals, and regression analysis. It helps generalize findings from a sample to a larger population.

11. Exploratory Data Analysis (EDA)

EDA   focuses on exploring and understanding the data without preconceived hypotheses. It involves visualizations, summary statistics, and data profiling techniques to uncover patterns, relationships, and interesting features. It helps generate hypotheses for further analysis.

12. Diagnostic Analysis

Diagnostic analysis aims to understand the cause-and-effect relationships within the data. It investigates the factors or variables that contribute to specific outcomes or behaviors. Techniques such as regression analysis, ANOVA (Analysis of Variance), or correlation analysis are commonly used in diagnostic analysis.

13. Predictive Analysis

Predictive analysis   involves using historical data to make predictions or forecasts about future outcomes. It utilizes statistical modeling techniques, machine learning algorithms, and time series analysis to identify patterns and build predictive models. It is often used for forecasting sales, predicting customer behavior, or estimating risk.

14. Prescriptive Analysis

Prescriptive analysis goes beyond predictive analysis by recommending actions or decisions based on the predictions. It combines historical data, optimization algorithms, and business rules to provide actionable insights and optimize outcomes. It helps in decision-making and resource allocation.

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Data analysis is a versatile and indispensable tool that finds applications across various industries and domains. Its ability to extract actionable insights from data has made it a fundamental component of decision-making and problem-solving. Let's explore some of the key applications of data analysis:

1. Business and Marketing

  • Market Research: Data analysis helps businesses understand market trends, consumer preferences, and competitive landscapes. It aids in identifying opportunities for product development, pricing strategies, and market expansion.
  • Sales Forecasting: Data analysis models can predict future sales based on historical data, seasonality, and external factors. This helps businesses optimize inventory management and resource allocation.

2. Healthcare and Life Sciences

  • Disease Diagnosis: Data analysis is vital in medical diagnostics, from interpreting medical images (e.g., MRI, X-rays) to analyzing patient records. Machine learning models can assist in early disease detection.
  • Drug Discovery: Pharmaceutical companies use data analysis to identify potential drug candidates, predict their efficacy, and optimize clinical trials.
  • Genomics and Personalized Medicine: Genomic data analysis enables personalized treatment plans by identifying genetic markers that influence disease susceptibility and response to therapies.
  • Risk Management: Financial institutions use data analysis to assess credit risk, detect fraudulent activities, and model market risks.
  • Algorithmic Trading: Data analysis is integral to developing trading algorithms that analyze market data and execute trades automatically based on predefined strategies.
  • Fraud Detection: Credit card companies and banks employ data analysis to identify unusual transaction patterns and detect fraudulent activities in real time.

4. Manufacturing and Supply Chain

  • Quality Control: Data analysis monitors and controls product quality on manufacturing lines. It helps detect defects and ensure consistency in production processes.
  • Inventory Optimization: By analyzing demand patterns and supply chain data, businesses can optimize inventory levels, reduce carrying costs, and ensure timely deliveries.

5. Social Sciences and Academia

  • Social Research: Researchers in social sciences analyze survey data, interviews, and textual data to study human behavior, attitudes, and trends. It helps in policy development and understanding societal issues.
  • Academic Research: Data analysis is crucial to scientific physics, biology, and environmental science research. It assists in interpreting experimental results and drawing conclusions.

6. Internet and Technology

  • Search Engines: Google uses complex data analysis algorithms to retrieve and rank search results based on user behavior and relevance.
  • Recommendation Systems: Services like Netflix and Amazon leverage data analysis to recommend content and products to users based on their past preferences and behaviors.

7. Environmental Science

  • Climate Modeling: Data analysis is essential in climate science. It analyzes temperature, precipitation, and other environmental data. It helps in understanding climate patterns and predicting future trends.
  • Environmental Monitoring: Remote sensing data analysis monitors ecological changes, including deforestation, water quality, and air pollution.

Learn The Latest Trends in Data Analytics!

Learn The Latest Trends in Data Analytics!

1. Descriptive Statistics

Descriptive statistics provide a snapshot of a dataset's central tendencies and variability. These techniques help summarize and understand the data's basic characteristics.

2. Inferential Statistics

Inferential statistics involve making predictions or inferences based on a sample of data. Techniques include hypothesis testing, confidence intervals, and regression analysis. These methods are crucial for drawing conclusions from data and assessing the significance of findings.

3. Regression Analysis

It explores the relationship between one or more independent variables and a dependent variable. It is widely used for prediction and understanding causal links. Linear, logistic, and multiple regression are common in various fields.

4. Clustering Analysis

It is an unsupervised learning method that groups similar data points. K-means clustering and hierarchical clustering are examples. This technique is used for customer segmentation, anomaly detection, and pattern recognition.

5. Classification Analysis

Classification analysis assigns data points to predefined categories or classes. It's often used in applications like spam email detection, image recognition, and sentiment analysis. Popular algorithms include decision trees, support vector machines, and neural networks.

6. Time Series Analysis

Time series analysis deals with data collected over time, making it suitable for forecasting and trend analysis. Techniques like moving averages, autoregressive integrated moving averages (ARIMA), and exponential smoothing are applied in fields like finance, economics, and weather forecasting.

7. Text Analysis (Natural Language Processing - NLP)

Text analysis techniques, part of NLP , enable extracting insights from textual data. These methods include sentiment analysis, topic modeling, and named entity recognition. Text analysis is widely used for analyzing customer reviews, social media content, and news articles.

8. Principal Component Analysis

It is a dimensionality reduction technique that simplifies complex datasets while retaining important information. It transforms correlated variables into a set of linearly uncorrelated variables, making it easier to analyze and visualize high-dimensional data.

Data Analyst Master's Program

Data Analyst Master's Program

9. Anomaly Detection

Anomaly detection identifies unusual patterns or outliers in data. It's critical in fraud detection, network security, and quality control. Techniques like statistical methods, clustering-based approaches, and machine learning algorithms are employed for anomaly detection.

10. Data Mining

Data mining involves the automated discovery of patterns, associations, and relationships within large datasets. Techniques like association rule mining, frequent pattern analysis, and decision tree mining extract valuable knowledge from data.

11. Machine Learning and Deep Learning

ML and deep learning algorithms are applied for predictive modeling, classification, and regression tasks. Techniques like random forests, support vector machines, and convolutional neural networks (CNNs) have revolutionized various industries, including healthcare, finance, and image recognition.

12. Geographic Information Systems (GIS) Analysis

GIS analysis combines geographical data with spatial analysis techniques to solve location-based problems. It's widely used in urban planning, environmental management, and disaster response.

  • Uncovering Patterns and Trends: Data analysis allows researchers to identify patterns, trends, and relationships within the data. By examining these patterns, researchers can better understand the phenomena under investigation. For example, in epidemiological research, data analysis can reveal the trends and patterns of disease outbreaks, helping public health officials take proactive measures.
  • Testing Hypotheses: Research often involves formulating hypotheses and testing them. Data analysis provides the means to evaluate hypotheses rigorously. Through statistical tests and inferential analysis, researchers can determine whether the observed patterns in the data are statistically significant or simply due to chance.
  • Making Informed Conclusions: Data analysis helps researchers draw meaningful and evidence-based conclusions from their research findings. It provides a quantitative basis for making claims and recommendations. In academic research, these conclusions form the basis for scholarly publications and contribute to the body of knowledge in a particular field.
  • Enhancing Data Quality: Data analysis includes data cleaning and validation processes that improve the quality and reliability of the dataset. Identifying and addressing errors, missing values, and outliers ensures that the research results accurately reflect the phenomena being studied.
  • Supporting Decision-Making: In applied research, data analysis assists decision-makers in various sectors, such as business, government, and healthcare. Policy decisions, marketing strategies, and resource allocations are often based on research findings.
  • Identifying Outliers and Anomalies: Outliers and anomalies in data can hold valuable information or indicate errors. Data analysis techniques can help identify these exceptional cases, whether medical diagnoses, financial fraud detection, or product quality control.
  • Revealing Insights: Research data often contain hidden insights that are not immediately apparent. Data analysis techniques, such as clustering or text analysis, can uncover these insights. For example, social media data sentiment analysis can reveal public sentiment and trends on various topics in social sciences.
  • Forecasting and Prediction: Data analysis allows for the development of predictive models. Researchers can use historical data to build models forecasting future trends or outcomes. This is valuable in fields like finance for stock price predictions, meteorology for weather forecasting, and epidemiology for disease spread projections.
  • Optimizing Resources: Research often involves resource allocation. Data analysis helps researchers and organizations optimize resource use by identifying areas where improvements can be made, or costs can be reduced.
  • Continuous Improvement: Data analysis supports the iterative nature of research. Researchers can analyze data, draw conclusions, and refine their hypotheses or research designs based on their findings. This cycle of analysis and refinement leads to continuous improvement in research methods and understanding.

Data analysis is an ever-evolving field driven by technological advancements. The future of data analysis promises exciting developments that will reshape how data is collected, processed, and utilized. Here are some of the key trends of data analysis:

1. Artificial Intelligence and Machine Learning Integration

Artificial intelligence (AI) and machine learning (ML) are expected to play a central role in data analysis. These technologies can automate complex data processing tasks, identify patterns at scale, and make highly accurate predictions. AI-driven analytics tools will become more accessible, enabling organizations to harness the power of ML without requiring extensive expertise.

2. Augmented Analytics

Augmented analytics combines AI and natural language processing (NLP) to assist data analysts in finding insights. These tools can automatically generate narratives, suggest visualizations, and highlight important trends within data. They enhance the speed and efficiency of data analysis, making it more accessible to a broader audience.

3. Data Privacy and Ethical Considerations

As data collection becomes more pervasive, privacy concerns and ethical considerations will gain prominence. Future data analysis trends will prioritize responsible data handling, transparency, and compliance with regulations like GDPR . Differential privacy techniques and data anonymization will be crucial in balancing data utility with privacy protection.

4. Real-time and Streaming Data Analysis

The demand for real-time insights will drive the adoption of real-time and streaming data analysis. Organizations will leverage technologies like Apache Kafka and Apache Flink to process and analyze data as it is generated. This trend is essential for fraud detection, IoT analytics, and monitoring systems.

5. Quantum Computing

It can potentially revolutionize data analysis by solving complex problems exponentially faster than classical computers. Although quantum computing is in its infancy, its impact on optimization, cryptography , and simulations will be significant once practical quantum computers become available.

6. Edge Analytics

With the proliferation of edge devices in the Internet of Things (IoT), data analysis is moving closer to the data source. Edge analytics allows for real-time processing and decision-making at the network's edge, reducing latency and bandwidth requirements.

7. Explainable AI (XAI)

Interpretable and explainable AI models will become crucial, especially in applications where trust and transparency are paramount. XAI techniques aim to make AI decisions more understandable and accountable, which is critical in healthcare and finance.

8. Data Democratization

The future of data analysis will see more democratization of data access and analysis tools. Non-technical users will have easier access to data and analytics through intuitive interfaces and self-service BI tools , reducing the reliance on data specialists.

9. Advanced Data Visualization

Data visualization tools will continue to evolve, offering more interactivity, 3D visualization, and augmented reality (AR) capabilities. Advanced visualizations will help users explore data in new and immersive ways.

10. Ethnographic Data Analysis

Ethnographic data analysis will gain importance as organizations seek to understand human behavior, cultural dynamics, and social trends. This qualitative data analysis approach and quantitative methods will provide a holistic understanding of complex issues.

11. Data Analytics Ethics and Bias Mitigation

Ethical considerations in data analysis will remain a key trend. Efforts to identify and mitigate bias in algorithms and models will become standard practice, ensuring fair and equitable outcomes.

Our Data Analytics courses have been meticulously crafted to equip you with the necessary skills and knowledge to thrive in this swiftly expanding industry. Our instructors will lead you through immersive, hands-on projects, real-world simulations, and illuminating case studies, ensuring you gain the practical expertise necessary for success. Through our courses, you will acquire the ability to dissect data, craft enlightening reports, and make data-driven choices that have the potential to steer businesses toward prosperity.

Having addressed the question of what is data analysis, if you're considering a career in data analytics, it's advisable to begin by researching the prerequisites for becoming a data analyst. You may also want to explore the Post Graduate Program in Data Analytics offered in collaboration with Purdue University. This program offers a practical learning experience through real-world case studies and projects aligned with industry needs. It provides comprehensive exposure to the essential technologies and skills currently employed in the field of data analytics.

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1. What is the difference between data analysis and data science? 

Data analysis primarily involves extracting meaningful insights from existing data using statistical techniques and visualization tools. Whereas, data science encompasses a broader spectrum, incorporating data analysis as a subset while involving machine learning, deep learning, and predictive modeling to build data-driven solutions and algorithms.

2. What are the common mistakes to avoid in data analysis?

Common mistakes to avoid in data analysis include neglecting data quality issues, failing to define clear objectives, overcomplicating visualizations, not considering algorithmic biases, and disregarding the importance of proper data preprocessing and cleaning. Additionally, avoiding making unwarranted assumptions and misinterpreting correlation as causation in your analysis is crucial.

Data Science & Business Analytics Courses Duration and Fees

Data Science & Business Analytics programs typically range from a few weeks to several months, with fees varying based on program and institution.

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What is Data Analysis?

Data are everywhere nowadays. And with each passing year, the amount of data we are producing will only continue to increase.

There is a large amount of data available, but what do we do with all that data? How is it all used? And what does all that data mean?

It’s not much use if we just collect and store data in a spreadsheet or database and don't look at it, explor it, or research it.

Data analysts use tools and processes to derive meaning from data. They are responsible for collecting, manipulating, investigating, analyzing, gathering insights, and gaining knowledge from it.

This is one of the reasons data analysts are very high in demand: they play an integral role in business and science.

In this article, I will first go over what data analysis means as a term and explain why it is so important.

I will also break down the data analysis process and list some of the necessary skills required for conducting data analysis.

Here is an overview of what we will cover:

  • What is data?
  • What is data analysis?
  • Effective customer targeting
  • Measure success and performance
  • Problem solving
  • Step 1: recognising and identifying the questions that need answering
  • Step 2: collecting raw data
  • Step 3: cleaning the data
  • Step 4: analyzing the data
  • Step 5: sharing the results
  • A good grasp of maths and statistics

Knowledge of SQL and Relational Databases

  • Knowledge of a programming language

Knowledge of data visualization tools

Knowledge of excel, what is data meaning and definition of data.

Data refers to collections of facts and individual pieces of information.

Data is vital for decision-making, planning, and even telling a story.

There are two broad and general types of data:

  • Qualitative data
  • Quantitative data

Qualitative data is data expressed in non-numerical characters.

It is expressed as images, videos, text documents, or audio.

This type of data can’t be measured or counted.

It is used to determine how people feel about something – it’s about people's feelings, motivations, opinions, perceptions and involves bias.

It is descriptive and aims to answer questions such as ‘Why’, ‘How’, and ‘What’.

Qualitative data is gathered from observations, surveys, or user interviews.

Quantitative data is expressed in numerical characters.

This type of data is countable, measurable, and comparable.

It is about amounts of numbers and involves things such as quantity and the average of numbers.

It aims to answer questions such as ‘How much, ‘How many’, ‘How often’, ‘and 'How long’.

The act of collecting, analyzing, and interpreting quantitative data is known as performing statistical analysis.

Statistical analysis helps uncover underlying patterns and trends in data.

What Is Data Analysis? A Definition For Beginners

Data analysis is the act of turning raw, messy data into useful insights by cleaning the data up, transforming it, manipulating it, and inspecting it.

The insights gathered from the data are then presented visually in the form of charts, graphs, or dashboards.

The insights discovered can help aid the company’s or organization’s growth. Decision-makers will be able to come to an actionable conclusion and make the right business decisions.

Extracting knowledge from raw data will help the company/organization take steps towards achieving greater customer reach, improving performance, and increasing profit.

At its core, data analysis is about identifying and predicting trends and figuring out patterns, correlations, and relationships in the available data, and finding solutions to complex problems.

Why Is Data Analysis Important?

Data equals knowledge.

This means that data analysis is integral for every business.

It can be useful and greatly beneficial for every department, whether it's administration, accounting, logistics, marketing, design, or engineering, to name a few.

Below I will explain why exploring data and giving data context and meaning is really important.

Data Analysis Improves Customer Targeting

By analyzing data, you understand your competitors, and you will be able to match your product/service to the current market needs.

It also helps you determine the appropriate audience and demographic best suited to your product or service.

This way, you will be able to come up with an effective pricing strategy to make sure that your product/service will be profitable.

You will also be able to create more targeted campaigns and know what methods and forms of advertising and content to use to reach your audience directly and effectively.

Knowing the right audience for your product or service will transform your whole strategy. It will become more customer-oriented and customized to fit customers' needs.

Essentially, with the appropriate information and tools, you will be able to figure out how your product or service can be of value and high quality.

You'll also be able to make sure that your product or service helps solve a problem for your customers.

This is especially important in the product development phases since it cuts down on expenses and saves time.

Data Analysis Measures Success and Performance

By analyzing data, you can measure how well your product/service performs in the market compared to others.

You are able to identify the stronger areas that have seen the most success and desired results. And you will be able to identify weaker areas that are facing problems.

Additionally, you can predict what areas could possibly face problems before the problem actually occurs. This way, you can take action and prevent the problem from happening.

Analyzing data will give you a better idea of what you should focus more on and what you should focus less on going forward.

By creating performance maps, you can then go on to set goals and identify potential opportunities.

Data Analysis Can Aid Problem Solving

By performing data analysis on relevant, correct, and accurate data, you will have a better understanding of the right choices you need to make and how to make more informed and wiser decisions.

Data analysis means having better insights, which helps improve decision-making and leads to solving problems.

All the above will help a business grow.

Not analyzing data, or having insufficient data, could be one of the reasons why your business is not growing.

If that is the case, performing data analysis will help you come up with a more effective strategy for the future.

And if your business is growing, analyzing data will help it grow even further.

It will help reach its full potential and meet different goals – such as boosting customer retention, finding new customers, or providing a smoother and more pleasant customer experience.

An Overview Of The Data Analysis Process

Step 1: recognising and identifying the questions that need answering.

The first step in the data analysis process is setting a clear objective.

Before setting out to gather a large amount of data, it is important to think of why you are actually performing the data analysis in the first place.

What problem are you trying to solve?

What is the purpose of this data analysis?

What are you trying to do?

What do you want to achieve?

What is the end goal?

What do you want to gain from the analysis?

Why do you even need data analysis?

At this stage, it is paramount to have an insight and understanding of your business goals.

Start by defining the right questions you want to answer and the immediate and long-term business goals.

Identify what is needed for the analysis, what kind of data you would need, what data you want to track and measure, and think of a specific problem you want to solve.

Step 2: Collecting Raw Data

The next step is to identify what type of data you want to collect – whether it will be qualitative (non-numerical, descriptive ) or quantitative (numerical).

The way you go about collecting the data and the sources you gather from will depend on whether it is qualitative or quantitative.

Some of the ways you could collect relevant and suitable data are:

  • By viewing the results of user groups, surveys, forms, questionnaires, internal documents, and interviews that have already been conducted in the business.
  • By viewing customer reviews and feedback on customer satisfaction.
  • By viewing transactions and purchase history records, as well as sales and financial figure reports created by the finance or marketing department of the business.
  • By using a customer relationship management system (CRM) in the company.
  • By monitoring website and social media activity and monthly visitors.
  • By monitoring social media engagement.
  • By tracking commonly searched keywords and search queries.
  • By checking which ads are regularly clicked on.
  • By checking customer conversion rates.
  • By checking email open rates.
  • By comparing the company’s data to competitors using third-party services.
  • By querying a database.
  • By gathering data through open data sets using web scraping. Web scraping is the act of extracting and collecting data and content from websites.

Step 3: Cleaning The Data

Once you have gathered the data from multiple sources, it is important to understand the structure of that data.

It is also important to check if you have gathered all the data you needed and if any crucial data is missing.

If you used multiple sources for the data collection, your data will likely be unstructured.

Raw, unstructured data is not usable. Not all data is necessarily good data.

Cleaning data is the most important part of the data analysis process and one on which data analysts spend most of their time.

Data needs to be cleaned, which means correcting errors, polishing, and sorting through the data.

This could include:

  • Looking for outliers (values that are unusually big or small).
  • Fixing typos.
  • Removing errors.
  • Removing duplicate data.
  • Managing inconsistencies in the format.
  • Checking for missing values or correcting incorrect data.
  • Checking for inconsistencies
  • Getting rid of irrelevant data and data that is not useful or needed for the analysis.

This step will ensure that you are focusing on and analyzing the correct and appropriate data and that your data is high-quality.

If you analyze irrelevant or incorrect data, it will affect the results of your analysis and have a negative impact overall.

So, the accuracy of your end analysis will depend on this step.

Step 4: Analyzing The Data

The next step is to analyze the data based on the questions and objectives from step 1.

There are four different data analysis techniques used, and they depend on the goals and aims of the business:

  • Descriptive Analysis : This step is the initial and fundamental step in the analysis process. It provides a summary of the collected data and aims to answer the question: “ What happened?”. It goes over the key points in the data and emphasizes what has already taken place.
  • Diagnostic Analysis : This step is about using the collected data and trying to understand the cause behind the issue at hand and identify patterns. It aims to answer the question: “ Why has this happened?”.
  • Predictive Analysis : This step is about detecting and predicting future trends and is important for the future growth of the business. It aims to answer the question: “ What is likely to happen in the future?
  • Prescriptive Analysis: This step is about gathering all the insights from the three previous steps, making recommendations for the future, and creating an actionable plan. It aims to answer the question: “ What needs to be done? ”

Step 5: Sharing The Results

The last step is to interpret your findings.

This is usually done by creating reports, charts, graphs, or interactive dashboards using data visualization tools.

All the above will help support the presentation of your findings and the results of your analysis to stakeholders, business executives, and decision-makers.

Data analysts are storytellers, which means having strong communication skills is important.

They need to showcase the findings and present the results in a clear, concise, and straightforward way by taking the data and creating a narrative.

This step will influence decision-making and the future steps of the business.

What Skills Are Required For Data Analysis?

A good grasp of maths and statistics.

The amount of maths you will use as a data analyst will vary depending on the job. Some jobs may require working with maths more than others.

You don’t necessarily need to be a math wizard, but with that said, having at least a fundamental understanding of math basics can be of great help.

Here are some math courses to get you started:

  • College Algebra – Learn College Math Prerequisites with this Free 7-Hour Course
  • Precalculus – Learn College Math Prerequisites with this Free 5-Hour Course
  • Math for Programmers Course

Data analysts need to have good knowledge of statistics and probability for gathering and analyzing data, figuring out patterns, and drawing conclusions from the data.

To get started, take an intro to statistics course, and then you can move on to more advanced topics:

  • Learn College-level Statistics in this free 8-hour course
  • If you want to learn Data Science, take a few of these statistics classes

Data analysts need to know how to interact with relational databases to extract data.

A database is an electronic storage localization for data. Data can be easily retrieved and searched through.

A relational database is structured in format and all data items stored have pre-defined relationships with each other.

SQL stands for S tructured Q uery L anguage and is the language used for querying and interacting with relational databases.

By writing SQL queries you can perform CRUD (Create, Read, Update, and Delete) operations on data.

To learn SQL, check out the following resources:

  • SQL Commands Cheat Sheet – How to Learn SQL in 10 Minutes
  • Learn SQL – Free Relational Database Courses for Beginners
  • Relational Database Certification

Knowledge Of A Programming Language

To further organize and manipulate databases, data analysts benefit from knowing a programming language.

Two of the most popular ones used in the data analysis field are Python and R.

Python is a general-purpose programming language, and it is very beginner-friendly thanks to its syntax that resembles the English language. It is also one of the most used technical tools for data analysis.

Python offers a wealth of packages and libraries for data manipulation, such as Pandas and NumPy, as well as for data visualization, such as Matplotlib.

To get started, first see how to go about learning Python as a complete beginner .

Once you understand the fundamentals, you can move on to learning about Pandas, NumPy, and Matplotlib.

Here are some resources to get you started:

  • How to Get Started with Pandas in Python – a Beginner's Guide
  • The Ultimate Guide to the Pandas Library for Data Science in Python
  • The Ultimate Guide to the NumPy Package for Scientific Computing in Python
  • Learn NumPy and start doing scientific computing in Python
  • How to Analyze Data with Python, Pandas & Numpy - 10 Hour Course
  • Matplotlib Course – Learn Python Data Visualization
  • Python Data Science – A Free 12-Hour Course for Beginners. Learn Pandas, NumPy, Matplotlib, and More.

R is a language used for statistical analysis and data analysis. That said, it is not as beginner-friendly as Python.

To get started learning it, check out the following courses:

  • R Programming Language Explained
  • Learn R programming language basics in just 2 hours with this free course on statistical programming

Data visualization is the graphical interpretation and presentation of data.

This includes creating graphs, charts, interactive dashboards, or maps that can be easily shared with other team members and important stakeholders.

Data visualization tools are essentially used to tell a story with data and drive decision-making.

One of the most popular data visualization tools used is Tableau.

To learn Tableau, check out the following course:

  • Tableau for Data Science and Data Visualization - Crash Course

Excel is one of the most essential tools used in Data analysis.

It is used for storing, structuring, and formatting data, performing calculations, summarizing data and identifying trends, sorting data into categories, and creating reports.

You can also use Excel to create charts and graphs.

To learn how to use Excel, check out the following courses:

  • Learn Microsoft Excel - Full Video Course
  • Excel Classes Online – 11 Free Excel Training Courses
  • Data Analysis with Python for Excel Users Course

This marks the end of the article – thank you so much for making it to the end!

Hopefully this guide was helpful, and it gave you some insight into what data analysis is, why it is important, and what skills you need to enter the field.

Thank you for reading!

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What Is Data Analysis and How Can You Get Started?

Data analysis

On average, up to 55% of data collected by companies goes unused for analysis.

Perhaps businesses don’t know what to do with it. Or, they don’t know what they want to achieve by going through all their data. Maybe, they know how valuable it is, but don’t have the tools to organize, process, and analyze their data.

In this article, we’ll explain the different types of data you can analyze, walk you through the data analysis process , and explain how data analysis leads to smarter business decisions.

What Is Data Analysis?

  • Quantitative vs. Qualitative Data: What’s The Difference?

Data Analysis Methods, Techniques & Examples

Why is data analysis important.

  • The Data Analysis Process in 7 Steps
  • How to Get Started with Data Analysis

What is Unstructured Data?

Data analysis is the process of cleaning, analyzing, interpreting, and visualizing data using various techniques and business intelligence tools. Data analysis tools help you discover relevant insights that lead to smarter and more effective decision-making.

You’ll often see the terms data analysis and data analytics used interchangeably. But, there’s a slight difference between the two.

Data analytics is the overarching discipline and refers to the whole process of data management: data collecting, storing, organizing, and analyzing. It includes the tools and techniques used to deep-dive into data, as well as those used to communicate the results ‒ for example, data visualization tools.

Data analysis, on the other hand, focuses on the process of turning raw data into useful statistics, information, and explanations.

Quantitative vs Qualitative Data: What’s the Difference?

Qualitative data vs. quantitative data

There are two types of data that companies can analyze:

  • Numerical, quantifiable, or quantitative data
  • Textual or qualitative data.

The differences between qualitative and quantitative data, including a definition, types of data, what it answers, and examples of both data types.

Quantitative Data

Quantitative data deals with whole numbers, percentages, and statistics data analysis – data that can be expressed as a quantity.

Quantitative data is usually structured data that is pre-formatted and fits neatly into the columns and rows of spreadsheets. It responds to close-ended questions like “how many?”, “how often?” and “how much?”.

Structured query language (SQL) is a programming language used to communicate with and organize databases and particularly useful when handling quantitative data. You can easily perform quantitative data analysis in Excel to answer questions like:

  • How many users signed up in the month of May?
  • What was the churn rate compared to the previous year?
  • What was the drop-off rate at the shopping cart?

Qualitative Data

Qualitative data deals with features or characteristics – data that describes something and is expressed in words.

Also known as unstructured data because it has no format or pre-configured design, qualitative data allows you to understand the “why?” behind numbers and statistics and provides insights into behavior and patterns.

AI data analysis platforms, like MonkeyLearn are great for qualitative data because they use techniques like natural language processing (NLP) to break down unstructured data, so that it can be understood and analyzed by machines.

General purpose programming languages, like Python, are also ideal for qualitative data analysis because they are much more open-ended and can be used to call or integrate with a variety of data analysis tools.

With qualitative data analysis, you can answer questions like:

  • Why were there fewer app users in the month of May?
  • Why was the churn rate higher than last year?
  • Why was the drop-off rate so high at the cart?

Traditionally, companies have focused more on quantifiable data because it’s easier to analyze through aggregation, regression analysis, and statistical analysis. However, with breakthroughs in linguistic and structural data analysis techniques, companies are now able to mine qualitative data for even more granular insights – adding an extra layer of analysis to their data.

Data analysis types

The six top methods for data analysis:

  • Text Analysis
  • Data Mining
  • Statistical Analysis
  • Diagnostic Analysis
  • Predictive Analysis
  • Prescriptive Analysis

1. Text Analysis

Text analysis (or the overall study, called text analytics), combines statistical and linguistic analysis methods to extract and classify information to perform.

The goal of text analysis is to transform natural spoken or written human language into machine-readable data. By understanding subtle nuances, like emotions and context, text analysis tools can sort qualitative data by topic, emotions, keywords, and intent. First, though, you’ll need to train machine learning algorithms to understand the subtleties of human language before they can make decisions independently.

For example, the word ‘bright’ has different meanings depending on its context:

The sun is very bright

She’s extremely bright

You’ve bright ened up my day

By tagging text examples and feeding them to text analysis tools, machines will be able to understand different meanings of the same word and their context when analyzing unseen text.

Businesses might use this type of analysis to read through huge amounts of text data for “opinion polarity” (positive, negative, neutral, etc.).

2. Data Mining

Data mining, also known as knowledge discovery in data (KDD) is the process of detecting anomalies, patterns, and relationships to predict outcomes.

It involves techniques like clustering (grouping similar objects), sequential pattern mining (finding frequent sequences of objects or events), and anomaly detection (spotting outliers, rare items, or unusual records).

Applications of data mining include predicting how markets will behave, allowing businesses to anticipate customer needs and be proactive.

3. Statistical Analysis

Statistical analysis, used on its own, involves exploring and presenting huge amounts of quantitative data to detect trends and patterns. While statistical analysis includes a wide range of techniques, we can split them into two main categories:

  • Descriptive statistics
  • Inferential statistics

Descriptive statistics data analysis describes describes, summarizes, and visualizes the basic features of data, through charts and reports. There are four main measures of descriptive statistics that form the basis of nearly every quantitative data analysis:

  • Frequency: count, percent
  • Central tendency: mean, median, and mode
  • Dispersion: range, variance, standard deviation
  • Position: percentile ranks, quartile ranks.

Inferential statistics data analysis is used to draw conclusions about an entire population (whether a class of students or the entire population of a country), based on samples that accurately represent the population.

The most common methodologies of inferential statistics are hypothesis tests and estimation theories.

Statistical analysis is a great way for organizations to carry out market research. Regression analysis , for example, is a statistical technique that calculates the relationship between two (or more) variables, like the number of visitors to your site and money spent on marketing. This will help you detect the precise impact of a change in marketing spend.

4. Diagnostic Analysis

Diagnostic analysis, also known as root cause analysis , aims to answer the question: “Why did something happen?” It’s an exploratory type of analysis that identifies anomalies and uncovers patterns and stories in your data.

Maybe you’ve noticed a sudden drop in customer complaints. But, why is that? Did you improve your customer support process, hire new staff, or improve a certain feature? These are all valid questions and diagnostic analysis will help you determine whether there’s a high or low correlation between data points and possible causes.

5. Predictive Analysis

Building a predictive analysis model can be a true game-changer for organizations. This type of analysis allows you to combine demographic data, purchase data, and other sources of information to answer: “What is likely to happen in the future?”

By using this type of analysis, you can anticipate customer needs, predict customer churn, and determine how many leads can be converted to sales. For governments, predictive analysis is increasingly important because it allows them to target likely problems before they snowball.

6. Prescriptive Analysis

Prescriptive analysis combines all the data and insights that you have and turns them into actionable insights. In a nutshell, it shows the best course of action to follow in a given scenario.

This type of analysis works by analyzing different scenarios and determining the most likely outcome of each. This way, you can make a decision based on what you hope to achieve.

This is the most advanced type of data analysis. Governments use prescriptive analysis to gain a better understanding of the likelihood of worst-case scenarios, where the cost of human error is high. Based on the results from this type of analysis, they can create an effective response plan.

Prescriptive analysis is also ideal for marketing campaigns to combine all customer analytics, past marketing data, and competitive analysis to decide the best course of action.

Read more about data analysis examples and applications .

From preparing for worst-case scenarios to improving services and products, all types of data analysis can help businesses make better decisions and create data-driven strategies.

By gaining first-hand insight into what’s wrong and why, leaders can define more effective strategies to improve processes, prevent problems, detect growth opportunities, and decide where to focus investments.

Among other things, data analysis allows you to:

Predict Behavior

Enhance productivity.

  • Make Data-driven Decisions

Gain a Competitive Advantage

Analyzing historical and real-time data can reveal trends and patterns, and help you predict what will happen next.

Companies providing a service, for example, might analyze customer sentiment on social media to detect unhappy customers and predict when they’re at risk of churning. Then target these customers with promotions or perks in an attempt to retain them.

Another great way to predict customer behavior is by sending out customer satisfaction surveys at each stage of the customer journey. Analyzing the open-ended responses from a batch of survey responses can help you identify problem areas, predict if customers are about to churn , and even anticipate your customers’ needs.

Learn more about performing survey data analysis to predict customer behaviour.

Data analysis can help you improve daily processes and increase your team’s productivity while boosting public satisfaction.

By analyzing customer support tickets , you can detect keywords and topics, read for degree of urgency, and automatically route them to the correct teams right away. This will also help you uncover common customer complaints and questions, which might incite you to create a self-service knowledge base. Maybe, complaints mention how slow first-response times are, in which case you might implement a chatbot to solve up to 80% of routine queries .

By using data analysis to uncover problems that lead to delays and productivity loss, you can take concrete steps to create solutions.

Make Data-Driven Decisions

Instead of relying on intuition or experience, data gives you solid evidence to support decisions.

Customer feedback , for instance, is a goldmine of real-time insights that you can use to build data-driven initiatives, product roadmaps, and streamlined services. Is there a feature that you could add to improve your app? What frustrates customers most about your service?

Slack often makes decisions based on customer feedback , from social media, personal interviews, and online surveys, to understand how business teams use and interact with their product. They then translate this insight into new features and enhanced functionalities.

Through data analysis, you can learn what’s working well (and what’s not) for your own business. But you can also detect the weaknesses and strengths of your competition, uncovering opportunities for improvement, or unique angles for your marketing strategy.

Online reviews provide great insight into your competition. This public data source allows you to answer questions like: what do customers love or hate about your competitors’ products or services?

Crazy Egg, a website optimization tool, analyzes sentiment in their competitors’ reviews to understand what motivates people to choose a product or switch to another company.

Knowing exactly what customers need and expect allows you to create better products and experiences for them and stand out in a competitive market.

The Data Analysis Process in 7 Steps: Learn how to Analyze Data Effectively

data analysis Process

The data analysis process includes setting goals, collecting, cleaning, and analyzing your data. Then using the results to make striking visualizations and draw conclusions for immediately actionable insights.

There are huge amounts of useful data from emails, chatbots, internal reports, product reviews, social media, and all over the web. It can be downright intimidating, and insights from raw data aren’t clear-cut. Fortunately, with the right steps to analyze your data and the right data analysis tools, you can be on your way to actionable insights in no time.

Start your data analysis process in just seven steps:

  • Define Your Goals
  • Collect Your Data
  • Clean Your Data
  • Integrate Data Analysis Tools
  • Analyze Your Data
  • Visualize Your Data
  • Draw Conclusions

1. Define Your Goals

Every data-driven strategy should be linked to specific goals. What outcomes do you want to achieve? What specific issues would you like to address?

Whether you’re trying to gauge public sentiment, speed up response times in customer support, increase signups, or launch a new feature, having a set of clear objectives is key. Also, it will determine the type of data that you’ll need to collect and track.

Your goals should be aligned with the overall business objectives and follow the SMART criteria, meaning they should be specific, measurable, achievable, relevant, and time-bound.

2. Collect Your Data

Once you’ve set up your goals, it’s time to collect relevant data from various sources. Qualitative data sources can be found both internally and externally:

Internal Data Sources

Collected data that belongs to your company, which can be found in:

  • Databases, CRM systems (like Hubspot or Salesforce)
  • Help desk software (Zendesk, Freshdesk, Helpscout)
  • Email (Gmail, Front, Outlook)
  • Survey tools (Typeform, Google Forms)
  • Chat providers (Slack, Intercom, Drift).
  • NPS tracking software (Promoter.io)
  • Product management software (Hive, UserVoice, ProductBoard, Emails from your clients)

In most cases, it’s quite simple to export data from these tools into a CSV or Excel file. Even better, you can connect these data sources directly to data analysis tools through APIs or integrations step 4).

External Data Sources

Data generated outside your organization:

  • Social media platforms (Facebook, Twitter, Instagram, Tik Tok)
  • Online product reviews (G2 Crowd, Capterra, TrustPilot, app stores, Facebook Business page, etc.)
  • Quarterly reports
  • Blogs, forums, news, etc.

While it may seem more difficult to capture this data, there are tools that can help you get the job done:

  • APIs, which connect to social media platforms and extract historical or real-time data.
  • Web scraping tools to crawl a website and obtain relevant data. Some of these tools are code-free, such as Dexi.io , ParseHub , and Portia .

3. Clean Your Data

Before analyzing your data, you first need to clean your dataset ‒ especially when working with unstructured text.

This means that you need to get rid of the noise that usually appears in text-based data. For example, if you are classifying a series of product reviews, you should remove special characters, punctuation marks, stopwords ( and, too, she, they ), HTML tags, etc.

Correcting spelling, removing abbreviations, and applying lowercase to all your text will also help when data cleaning.

MonkeyLearn’s boilerplate extractor helps remove HTML elements from text, while the email cleaner & last reply extractor is useful for removing signatures and introductions.

Finally, if your customer feedback contains large chunks of text, we recommend using a tool to split your data into opinion units (or, smaller chunks of text). That way, you can analyze individual clauses and gain more granular results.

Paragraphs are likely to contain more than one sentiment and refer to more than one topic, like in this example:

Opinion units

4. Integrate Data Analysis Tools

Instead of investing hours manually tagging customer data, you can use text analysis tools and get the job done in minutes, no matter the size of your dataset.

Depending on your goals, you may opt for pre-trained text analysis solutions ‒ like this sentiment analyzer that analyzes text for opinion polarity:

Test with your own text

You can even build fully customized models to suit specific needs.

No-code, low-code SaaS tools, like MonkeyLearn , are a user-friendly data analysis solution and allow you to start immediately. And they’re often simple to connect to your existing stack with APIs or integrations.

Through APIs, you can connect machine learning models directly to your data source and analyze data in real-time.

MonkeyLearn provides a simple (yet powerful) API in multiple languages , as well as smart, code-free integrations that work seamlessly with some of your favorite tools.

5. Analyze Your Data

Data analysis software helps you understand and interpret qualitative data to achieve your goals.

This is the most important step in the data analysis process when you extract value from your data. Once you’ve integrated data analysis tools, you’ll be able to put them to work on your data.

You can choose text analysis tools to help you sort customer reviews by topic and sentiment, just like we did in this analysis of customer reviews mentioning Facebook .

Or, you can connect sentiment analysis tools to your helpdesks to analyze social data . This allows you to sort mentions by positive and negative, and prioritize those that are most urgent.

The text analysis models you choose will all depend on the problem you’re trying to solve.

6. Visualize Your Data

Data visualization tools present data analysis results in a way that’s attractive, clear, and easy to understand.

By using different data sources to create interactive charts, reports, and dashboards, you summarize your data and find insights, patterns, and relationships that may not be evident in a spreadsheet.

Business intelligence (BI) software and visualization tools help you simplify your data and create engaging stories to share with other team members and stakeholders.

MonkeyLearn Studio , the only all-in-one text analysis solution, will take you from data collection, to analysis, and, ultimately, data visualization in just a few steps.

The below is a MonkeyLearn Studio dashboard showing aspect-based sentiment analysis of Zoom customer reviews. Aspect-based sentiment analysis first categorizes reviews by topic (in this case, Usability, Support, Reliability, etc.), then by sentiment, so that you end up showing which aspects of your business really shine for the customer, and which may need some work:

MonkeyLearn Studio dashboard showing results for intent classification and sentiment analysis in charts and graphs.

Learn how to get started with Studio.

Other data visualization tools on the market include Tableau and Google Data Studio. However, these tools don’t include built-in text analysis solutions. MonkeyLearn Studio allows you to do it all, right in the dashboard.

7. Draw Conclusions

Compare the results in your dashboard to the goals that you defined at the beginning of the process.

Granular insights that you gain from text analysis can be simplified in a dashboard and provide answers to questions that you set out to answer at the beginning of your analysis.

For example, why are customers switching to a competitor? How can I improve customer retention? Is a new campaign receiving good or bad press, and why?

How To Get Started With Data Analysis

How to get started with data analysis

When you’re ready to get started with data analysis, you’ll find that there are a couple of options: build your tools from scratch or use a SaaS (software as a service) solution. It’s the eternal build vs. buy debate.

There are many open-source libraries that developers can use to build machine learning models for data analysis. While they’re free, they’ll take time to implement and require complex and costly infrastructure, not to mention large upfront investments. You’ll probably end up hiring a team of developers and data scientists if you decide to build your own solution.

SaaS tools, on the other hand, are a simple and cost-effective alternative. These tools are cloud-based and ready-to-use, allowing you to perform a variety of tasks, from text analysis to data visualization, in next to no time.

Some of the benefits of buying SaaS tools over building your text analysis software include:

Lower costs & easily scalable. While open-source software is free, you’ll need a team of machine learning experts to set up the infrastructure, build complex algorithms, and test your tools. SaaS tools, on the other hand, are ready to use solutions and no-code, low-code options make it easy for non-technical users to get started with data analysis tools. That means you don’t need to hire expert staff, and you won’t waste time waiting for a solution to be built. You can easily scale up or down with SaaS tools, which offer various plans depending on the amount of data you need to analyze.

Quick to set up. In such a competitive market, businesses need to move fast to stay relevant. With SaaS tools, you don’t have to spend months developing and fine-tuning your machine learning models. Instead, it takes less than a month to implement out-of-the-box data analysis solutions, so you can start gaining insights and creating better customer experiences right away.

Maintenance costs included. When you build your own data analysis software, you need to take into account maintenance costs. You’ll need to keep a permanent team of experts on to tweak hyperparameters, update models, and maintain on-site infrastructure. If you choose to buy cloud-based data analysis software, you won’t have to worry about additional high maintenance costs. They’re included in the monthly license fee. And, since SaaS data analysis tools are in the cloud, there’s no physical software to maintain or repair.

Analyze Data With MonkeyLearn Studio

Boasting an intuitive no-code interface, MonkeyLearn Studio allows you to start analyzing and visualizing data right away.

Studio data analytics dashboard

Request a demo then follow the steps below:

1. Choose a template. Go to the dashboard and choose a template that best fits your use case. Or, create your own. Each template combines different pre-trained text analysis models depending on the type of analysis you want to carry out.

Choose a template

You can also build your custom models in a simple graphic user interface – completely code-free. This is the best option If you require data analysis tools that can understand domain-specific vocabulary.

2. Import your data. Upload an Excel or CSV file with your data, or connect to one of the many data integrations (such as Twitter, Zendesk, or Gmail).

Import your data

3. Run the analysis.

Once you’ve uploaded your data, your analysis will begin.

Run the analysis

4. Visualize your data.

You’ll be able to visualize the results of your analysis in real-time, in a customizable and striking dashboard.

Sentiment and topic data visualizations in an analytics dashboard

Start Your Data Analysis Journey

Data analysis helps businesses explore customer data and find insights to support and guide their decision making.

With data analysis tools , businesses can sift through large amounts of qualitative data in just minutes. This frees up staff from repetitive and time-consuming tasks and helps them obtain real-time insights about their customers. Knowing exactly what makes your customers tick helps you design better experiences for them, and provides you with a competitive advantage.

Start gaining more granular insights by combining different data analysis techniques and connecting your results to data visualization tools.

SaaS tools, like MonkeyLearn Studio , make it easy to build custom data analysis solutions and integrate them with your apps. Take a look at MonkeyLearn Studio’s public dashboard, play around a bit, and see just how easy it is to use.

Ready to get started? Request a demo and discover how you can power up your data.

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What Is Data Analysis and Why Is It Important?

What is data analysis? We explain data mining, analytics, and data visualization in simple to understand terms.

The world is becoming more and more data-driven, with endless amounts of data available to work with. Big companies like Google and Microsoft use data to make decisions, but they're not the only ones.

Is it important? Absolutely!

Data analysis is used by small businesses, retail companies, in medicine, and even in the world of sports. It's a universal language and more important than ever before. It seems like an advanced concept but data analysis is really just a few ideas put into practice.

What Is Data Analysis?

Data analysis is the process of evaluating data using analytical or statistical tools to discover useful information. Some of these tools are programming languages like R or Python. Microsoft Excel is also popular in the world of data analytics .

Once data is collected and sorted using these tools, the results are interpreted to make decisions. The end results can be delivered as a summary, or as a visual like a chart or graph.

The process of presenting data in visual form is known as data visualization . Data visualization tools make the job easier. Programs like Tableau or Microsoft Power BI give you many visuals that can bring data to life.

There are several data analysis methods including data mining, text analytics, and business intelligence.

How Is Data Analysis Performed?

Data Processing for Data Analysis

Data analysis is a big subject and can include some of these steps:

  • Defining Objectives: Start by outlining some clearly defined objectives. To get the best results out of the data, the objectives should be crystal clear.
  • Posing Questions: Figure out the questions you would like answered by the data. For example, do red sports cars get into accidents more often than others? Figure out which data analysis tools will get the best result for your question.
  • Data Collection: Collect data that is useful to answer the questions. In this example, data might be collected from a variety of sources like DMV or police accident reports, insurance claims and hospitalization details.
  • Data Scrubbing: Raw data may be collected in several different formats, with lots of junk values and clutter. The data is cleaned and converted so that data analysis tools can import it. It's not a glamorous step but it's very important.
  • Data Analysis: Import this new clean data into the data analysis tools. These tools allow you to explore the data, find patterns, and answer what-if questions. This is the payoff, this is where you find results!
  • Drawing Conclusions and Making Predictions: Draw conclusions from your data. These conclusions may be summarized in a report, visual, or both to get the right results.

Let's dig a little deeper into some concepts used in data analysis.

Data Mining

Data mining

Data mining is a method of data analysis for discovering patterns in large data sets using statistics, artificial intelligence, and machine learning. The goal is to turn data into business decisions.

What can you do with data mining? You can process large amounts of data to identify outliers and exclude them from decision making. Businesses can learn customer purchasing habits, or use clustering to find previously unknown groups within the data.

If you use email, you see another example of data mining to sort your mailbox. Email apps like Outlook or Gmail use this to categorize your emails as "spam" or "not spam".

Text Analytics

Text analytics

Data is not just limited to numbers, information can come from text information as well.

Text analytics is the process of finding useful information from text. You do this by processing raw text, making it readable by data analysis tools, and finding results and patterns. This is also known as text mining.

Excel does a great job with this. Excel has many formulas to work with text that can save you time when you go to work with the data.

Text mining can also collect information from the web, a database or a file system. What can you do with this text information? You can import email addresses and phone numbers to find patterns. You can even find frequencies of words in a document.

Business Intelligence

Business intelligence for data analysis

Business intelligence transforms data into intelligence used to make business decisions. It may be used in an organization's strategic and tactical decision making. It offers a way for companies to examine trends from collected data and get insights from it.

Business intelligence is used to do a lot of things:

  • Make decisions about product placement and pricing
  • Identify new markets for product
  • Create budgets and forecasts that make more money
  • Use visual tools such as heat maps, pivot tables, and geographical mapping to find the demand for a certain product

Data Visualization

data visualization for data analysis

Data visualization is the visual representation of data. Instead of presenting data in tables or databases, you present it in charts and graphs. It makes complex data more understandable, not to mention easier to look at.

Increasing amounts of data are being generated by applications you use (Also known as the "Internet of Things"). The amount of data (referred to as "big data") is pretty massive. Data visualization can turn millions of data points into simple visuals that make it easy to understand.

There are various ways to visualize data:

  • Using a data visualization tool like Tableau or Microsoft Power BI
  • Standard Excel graphs and charts
  • Interactive Excel graphs
  • For the web, a tool like D3.js built using JavaScript

The visualization of Google datasets is a great example of how big data can visually guide decision-making.

Data Analysis in Review

Data analysis is used to evaluate data with statistical tools to discover useful information. A variety of methods are used including data mining , text analytics, business intelligence, combining data sets , and data visualization.

The Power Query tool in Microsoft Excel is especially helpful for data analysis. If you want to familiarize yourself with it, read our guide to create your first Microsoft Power Query script .

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What Is Data Analytics?

Understanding data analytics, the role of data analytics.

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Data Analytics: What It Is, How It's Used, and 4 Basic Techniques

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Data analytics is the science of analyzing raw data to make conclusions about information. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption.

Key Takeaways

  • Data analytics is the science of analyzing raw data to make conclusions about that information.
  • Data analytics help a business optimize its performance, perform more efficiently, maximize profit, or make more strategically-guided decisions.
  • The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. 
  • Various approaches to data analytics include looking at what happened (descriptive analytics), why something happened (diagnostic analytics), what is going to happen (predictive analytics), or what should be done next (prescriptive analytics).
  • Data analytics relies on a variety of software tools including spreadsheets, data visualization, reporting tools, data mining programs, and open-source languages for the greatest data manipulation.

Data analytics is a broad term that encompasses many diverse types of data analysis. Any type of information can be subjected to data analytics techniques to get insight that can be used to improve things. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.

For example, manufacturing companies often record the runtime, downtime, and work queue for various machines and then analyze the data to better plan workloads so the machines operate closer to peak capacity.

Data analytics can do much more than point out bottlenecks in production. Gaming companies use data analytics to set reward schedules for players that keep the majority of players active in the game. Content companies use many of the same data analytics to keep you clicking, watching, or re-organizing content to get another view or another click.

Data analytics is important because it helps businesses optimize their performances. Implementing it into the business model means companies can help reduce costs by identifying more efficient ways of doing business and by storing large amounts of data.

A company can also use data analytics to make better business decisions and help analyze customer trends and satisfaction, which can lead to new and better products and services. 

Data Analysis Steps

The process involved in data analysis involves several steps:

  • The first step is to determine the data requirements or how the data is grouped. Data may be separated by age, demographic, income, or gender. Data values may be numerical or divided by category.
  • The second step in data analytics is the process of collecting it. This can be done through a variety of sources such as computers, online sources, cameras, environmental sources, or through personnel.
  • The data must be organized after it's collected so it can be analyzed. This may take place on a spreadsheet or other form of software that can take statistical data.
  • The data is then cleaned up before analysis. It's scrubbed and checked to ensure that there's no duplication or error and that it is not incomplete. This step helps correct any errors before it goes on to a data analyst to be analyzed.

Types of Data Analytics

Data analytics is broken down into four basic types:

  • Descriptive analytics: This describes what has happened over a given period of time. Have the number of views gone up? Are sales stronger this month than last?
  • Diagnostic analytics: This focuses more on why something happened. It involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales?
  • Predictive analytics: This moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year?
  • Prescriptive analytics: This suggests a course of action. We should add an evening shift to the brewery and rent an additional tank to increase output if the likelihood of a hot summer is measured as an average of these five weather models and the average is above 58%,

Data analytics underpins many quality control systems in the financial world, including the ever-popular Six Sigma program. It's nearly impossible to optimize something if you aren’t properly measuring it, whether it's your weight or the number of defects per million in a production line.

The sectors that have adopted the use of data analytics include the travel and hospitality industry where turnarounds can be quick. This industry can collect customer data and figure out where problems, if any, lie and how to fix them.

Healthcare combines the use of high volumes of structured and unstructured data and uses data analytics to make quick decisions. Similarly, the retail industry uses copious amounts of data to meet the ever-changing demands of shoppers. The information that retailers collect and analyze can help them identify trends, recommend products, and increase profits. 

The average total pay for a data analyst in the United States was just over $80,000 in August 2023.

Data Analytics Techniques

Data analysts can use several analytical methods and techniques to process data and extract information. Some of the most popular methods include:

  • Regression analysis entails analyzing the relationship between dependent variables to determine how a change in one may affect the change in another.
  • Factor analysis entails taking a large data set and shrinking it into a smaller data set. The goal of this maneuver is to attempt to discover hidden trends that would otherwise have been more difficult to see.
  • Cohort analysis is the process of breaking a data set into groups of similar data, often into a customer demographic. This allows data analysts and other users of data analytics to further dive into the numbers relating to a specific subset of data.
  • Monte Carlo simulations model the probability of different outcomes happening. They're often used for risk mitigation and loss prevention. These simulations incorporate multiple values and variables and often have greater forecasting capabilities than other data analytics approaches.
  • Time series analysis tracks data over time and solidifies the relationship between the value of a data point and the occurrence of the data point. This data analysis technique is usually used to spot cyclical trends or to project financial forecasts.

Data Analytics Tools

Data analytics has rapidly evolved in technological capabilities in addition to a broad range of mathematical and statistical approaches to crunching numbers. Data analysts have a broad range of software tools to help acquire data, store information, process data, and report findings.

Data analytics has always had loose ties to spreadsheets and Microsoft Excel. Data analysts also often interact with raw programming languages to transform and manipulate databases.

Data analysts also have help when reporting or communicating findings. Both Tableau and Power BI are data visualization and analysis tools used to compile information, perform data analytics, and distribute results via dashboards and reports.

Other tools are also emerging to assist data analysts. SAS is an analytics platform that can assist with data mining . Apache Spark is an open-source platform useful for processing large sets of data. Data analysts have a broad range of technological capabilities to further enhance the value they deliver to their company.

Data analytics can enhance operations, efficiency, and performance in numerous industries by shining a spotlight on patterns. Implementing these techniques can give companies and businesses a competitive edge. The process includes four basic steps of analysis.

Data Mining

As the name suggests, this step involves “mining” or gathering data and information from across a broad spectrum of sources. Various forms of information are then recreated into the same format so they can eventually be analyzed. The process can take a good bit of time, more than any other step.

Data Management

Data requires a database to contain, manage, and provide access to the information gathered through mining. The next step in data analytics is therefore the creation of such a database to manage the information. SQL was a common tool used for this purpose in the early days of data analytics and it's still widely used in 2023. Created in 1979, this computing language allows relational databases to be queried and the resulting data sets to be more easily analyzed.

Statistical Analysis

The third step is statistical analysis. It involves the interpretation of the gathered and stored data into models that will hopefully reveal trends that can be used to interpret future data. This is achieved through open-source programming languages such as Python. More specific tools for data analytics, like R, can be used for statistical analysis or graphical modeling.

Data Presentation

The results of the data analytics process are meant to be shared. The final step is formatting the data so it’s accessible to and understandable by others, particularly those individuals within a company who are responsible for growth, analysis, efficiency, and operations. Having access can be beneficial to shareholders as well.  

Importance and Uses of Data Analytics

Data analytics provide a critical component of a business’s probability of success. Gathering, sorting, analyzing, and presenting information can significantly enhance and benefit society, particularly in fields such as healthcare and crime prevention. But the uses of data analytics can be equally beneficial for small enterprises and startups that are looking for an edge over the business next door, albeit on a smaller scale,

Why Is Data Analytics Important?

Implementing data analytics into the business model means companies can help reduce costs by identifying more efficient ways of doing business. A company can also use data analytics to make better business decisions.

What Are the 4 Types of Data Analytics?

Data analytics is broken down into four basic types. Descriptive analytics describes what has happened over a given period. Diagnostic analytics focuses more on why something happened. Predictive analytics moves to what is likely going to happen in the near term. Finally, prescriptive analytics suggests a course of action.

Who Is Using Data Analytics?

Data analytics has been adopted by several sectors where turnarounds can be quick, such as the travel and hospitality industry. Healthcare is another sector that combines the use of high volumes of structured and unstructured data, and data analytics can help in making quick decisions. The retail industry also uses large amounts of data to meet the ever-changing demands of shoppers.

Data analytics helps individuals and organizations make sure of their data in a world that's increasingly becoming reliant on information and gathering statistics. A set of raw numbers can be transformed using a variety of tools and techniques, resulting in informative, educational insights that drive decision-making and thoughtful management.

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What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" ( Responsible Conduct in Data Management ). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible  data management , and maintaining "honest and accurate analysis" ( Responsible Conduct in Data Management ). 

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" ( OMB Circular 110 ). This broad definition can include information in many formats. 

Some examples of types of data are as follows:

  • Photographs 
  • Hand-written notes from field observation
  • Machine learning training data sets
  • Ethnographic interview transcripts
  • Sheet music
  • Scripts for plays and musicals 
  • Observations from laboratory experiments ( CMU Data 101 )

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis. 

Data analysis falls within the larger research data lifecycle, as seen below. 

( University of Virginia )

Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data. 

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods. 

Quantitative research typically involves numbers and "close-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures ( Creswell & Creswell, 2018 , p. 4). Quantitative analysis usually uses deductive reasoning. 

Qualitative  research typically involves words and "open-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" ( 2018 , p. 4). Thus, qualitative analysis usually invokes inductive reasoning. 

Mixed methods  research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" ( Creswell & Creswell, 2018 , p. 4). 

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Data Analysis: Definition, Types and Examples

Data analysis

Nowadays, data is collected at various stages of processes and transactions, which has the potential to improve the way we work significantly. However, to fully realize the value of data analysis, this data must be analyzed to gain valuable insights into improving products and services.

Data analysis consists aspect of making informed decisions in various industries. With the advancement of technology, it has become a dynamic and exciting field But what is it in simple words?

What is Data Analysis?

Data analysis is the science of examining data to conclude the information to make decisions or expand knowledge on various subjects. It consists of subjecting data to operations. This process happens to obtain precise conclusions to help us achieve our goals, such as operations that cannot be previously defined since data collection may reveal specific difficulties.

“A lot of this [data analysis] will help humans work smarter and faster because we have data on everything that happens.” –Daniel Burrus, business consultant and speaker on business and innovation issues.

Why is data analytics important?

Data analytics help businesses understand the target market faster, increase sales, reduce costs, increase revenue, and allow for better problem-solving. Data analysis is important for several reasons, as it plays a critical role in various aspects of modern businesses and organizations. Here are some key reasons why data analysis important is crucial:

Informed decision-making

Data analytics helps businesses make more informed and data-driven decisions. By analyzing data, organizations can gain insights into customer behavior, market trends, and operational performance, enabling them to make better choices that are supported by evidence rather than relying on intuition alone.

Identifying opportunities and challenges

Data analytics allows businesses to identify new opportunities for growth, product development, or market expansion. It also helps identify potential challenges and risks, allowing organizations to address them proactively.

Improving efficiency and productivity

Organizations can identify inefficiencies and bottlenecks by analyzing processes and performance data, leading to process optimization and improved productivity. This, in turn, can result in cost savings and better resource allocation.

Customer understanding and personalization

Data analytics enables businesses to understand their customers better, including their preferences, buying behaviors, and pain points. With this understanding, organizations can offer personalized products and services, enhancing customer satisfaction and loyalty.

Competitive advantage

Organizations that leverage data analytics effectively gain a competitive edge in today’s data-driven world. By analyzing data, businesses can identify unique insights and trends that better understand the market and their competitors, helping them stay ahead of the competition.

Performance tracking and evaluation

Data analytics allows organizations to track and measure their performance against key performance indicators (KPIs) and goals. This helps in evaluating the success of various strategies and initiatives, enabling continuous improvement.

Predictive analytics

Data analytics can be used for predictive modeling, helping organizations forecast future trends and outcomes. This is valuable for financial planning, demand forecasting, risk management, and proactive decision-making.

Data-driven innovation

Data analytics can fuel innovation by providing insights that lead to the development of new products, services, or business models. Innovations based on data analysis can lead to groundbreaking advancements and disruption in various industries.

Fraud detection and security

Data analytics can be used to detect anomalies and patterns indicative of fraudulent activities. It plays a crucial role in enhancing security and protecting businesses from financial losses and reputational risk .

Regulatory compliance

In many industries, regulations, and laws are mandatory. Data analytics can help organizations ensure that they meet these compliance requirements by tracking and auditing relevant data.

Types of data analysis

There are several types of data analysis, each with a specific purpose and method. Let’s talk about some significant types:

what is analysis data

Descriptive Analysis

Descriptive analysis is used to summarize and describe the main features of a dataset. It involves calculating measures of central tendency and dispersion to describe the data. The descriptive analysis provides a comprehensive overview of the data and insights into its properties and structure.

LEARN ABOUT: Descriptive Analysis

Inferential Analysis

The inferential analysis is used statistical analysis plan and testing to make inferences about the population parameters, such as the mean or proportion. This unit of analysis involves using models and hypothesis testing to make predictions and draw conclusions about the population.

LEARN ABOUT:   Statistical Analysis Methods

Predictive Analysis

Predictive analysis is used to predict future events or outcomes based on historical data and other relevant information. It involves using statistical models and machine learning algorithms to identify patterns in the data and make predictions about future outcomes.

Prescriptive Analysis

Prescriptive analysis is a decision-making analysis that uses mathematical modeling, optimization algorithms, and other data-driven techniques to identify the action for a given problem or situation. It combines mathematical models, data, and business constraints to find the best move or decision.

Text Analysis

Text analysis is a process of extracting meaningful information from unstructured text data. It involves a variety of techniques, including natural language processing (NLP), text mining, sentiment analysis, and topic modeling, to uncover insights and patterns in text data.

Diagnostic Analysis

The diagnostic analysis seeks to identify the root causes of specific events or outcomes. It is often used in troubleshooting problems or investigating anomalies in data.

LEARN ABOUT: Data Analytics Projects

Uses of data analysis

It is used in many industries regardless of the branch. It gives us the basis for making decisions or confirming a hypothesis.

A researcher or data analyst mainly performs data analysis to predict consumer behavior and help companies place their products and services in the market accordingly. For instance, sales data analysis can help you identify the product range not-so-popular in a specific demographic group. It can give you insights into tweaking your current marketing campaign to better connect with the target audience and address their needs. 

Human Resources

Organizations can use data analysis tools to offer a great experience to their employees and ensure an excellent work environment. They can also utilize the data to find out the best resources whose skill set matches the organizational goals.

Universities and academic institutions can perform the analysis to measure student performance and gather insights on how certain behaviors can further improve education.

Techniques for data analysis

It is essential to analyze raw data to understand it. We must resort to various data analysis techniques that depend on the type of information collected, so it is crucial to define the method before implementing it.

  • Qualitative data: Researchers collect qualitative data from the underlying emotions, body language, and expressions. Its foundation is the data interpretation of verbal responses. The most common ways of obtaining this information are through open-ended interviews, focus groups, and observation groups, where researchers generally analyze patterns in observations throughout the data collection phase.
  • Quantitative data: Quantitative data presents itself in numerical form. It focuses on tangible results.

Data analysis focuses on reaching a conclusion based solely on the researcher’s current knowledge. How you collect your data should relate to how you plan to analyze and use it. You also need to collect accurate and trustworthy information. 

Many data collection techniques exist, but experts’ most commonly used method is online surveys. It offers significant benefits, such as reducing time and money compared to traditional data collection methods.

Data analysis and data analytics are two interconnected but distinct processes in data science. Data analysis involves examining raw data using various techniques to uncover patterns, correlations, and insights. It’s about understanding historical data to make informed conclusions. On the other hand, data analytics goes a step further by utilizing those insights to predict future trends, prescribe actions, and guide decision-making.

At QuestionPro, we have an accurate tool that will help you professionally make better decisions.

Data Analysis Methods

The term data analysis technique has often been used interchangeably by professional researchers. Frequently people also throw out the previous analysis type. We’re hoping for this to be an important distinction between how and when data analyses are done. 

However, there are many different techniques that allow for data analysis. Here are some of the main common methods used for data analysis:

Descriptive Statistics

Descriptive statistics involves summarizing and describing the main features of a dataset, such as mean, median, mode, standard deviation, range, and percentiles. It provides a basic understanding of the data’s distribution and characteristics.

Inferential Statistics

Inferential statistics are used to make inferences and draw conclusions about a larger population based on a sample of data. It includes techniques like hypothesis testing, confidence intervals, and regression analysis.

Data Visualization

Data visualization is the graphical representation of data to help analysts and stakeholders understand patterns, trends, and insights. Common visualization techniques include bar charts, line graphs, scatter plots, heat maps, and pie charts.

Exploratory Data Analysis (EDA)

EDA involves analyzing and visualizing data to discover patterns, relationships, and potential outliers. It helps in gaining insights into the data before formal statistical testing.

Predictive Modeling

Predictive modeling uses algorithms and statistical techniques to build models that can make predictions about future outcomes based on historical data. Machine learning algorithms, such as decision trees, logistic regression, and neural networks, are commonly used for predictive modeling.

Time Series Analysis

Time series analysis is used to analyze data collected over time, such as stock prices, temperature readings, or sales data. It involves identifying trends and seasonality and forecasting future values.

Cluster Analysis

Cluster analysis is used to group similar data points together based on certain features or characteristics. It helps in identifying patterns and segmenting data into meaningful clusters.

Factor Analysis and Principal Component Analysis (PCA)

These techniques are used to reduce the dimensionality of data and identify underlying factors or components that explain the variance in the data.

Text Mining and Natural Language Processing (NLP)

Text mining and NLP techniques are used to analyze and extract information from unstructured text data, such as social media posts, customer reviews, or survey responses.

Qualitative Data Analysis

Qualitative data analysis involves interpreting non-numeric data, such as text, images, audio, or video. Techniques like content analysis, thematic analysis, and grounded theory are used to analyze qualitative data.

Quantitative Data Analysis

Quantitative analysis focuses on analyzing numerical data to discover relationships, trends, and patterns. This analysis often involves statistical methods.

Data Mining

Data mining involves discovering patterns, relationships, or insights from large datasets using various algorithms and techniques.

Regression Analysis

Regression analysis is used to model the relationship between a dependent variable and one or more independent variables. It helps understand how changes in one variable impact the other(s).

Step-by-step guide data analysis

With these five steps in your data analysis process, you will make better decisions for your business because data that has been well collected and analyzed support your choices.

LEARN ABOUT: Data Mining Techniques

steps to data analysis

Step 1: Define your questions

Start by selecting the right questions. Questions should be measurable, clear, and concise. Design your questions to qualify or disqualify possible solutions to your specific problem.

Step 2: Establish measurement priorities

This step divides into two sub-steps:

  • Decide what to measure: Analyze what kind of data you need.
  • Decide how to measure it: Thinking about how to measure your data is just as important, especially before the data collection phase, because your measurement process supports or discredits your thematic analysis later on.

Step 3: Collect data

With the question clearly defined and your measurement priorities established, now it’s time to collect your data. As you manage and organize your data, remember to keep these essential points in mind:

  • Before collecting new data, determine what information you could gather from existing databases or sources.
  • Determine a storage and file naming system to help all team members collaborate in advance. This process saves time and prevents team members from collecting the same information twice.
  • If you need to collect data through surveys, observation, or interviews, develop a questionnaire in advance to ensure consistency and save time.
  • Keep the collected data organized with a log of collection dates, and add any source notes as you go along.

Step 4: Analyze the data

Once you’ve collected the correct data to answer your Step 1 question, it’s time to conduct a deeper statistical analysis . Find relationships, identify trends, and sort and filter your data according to variables. You will find the exact data you need as you analyze the data.

Step 5: Interpret the results

After analyzing the data and possibly conducting further research, it is finally time to interpret the results. Ask yourself these key questions:

  • Does the data answer your original question? How?
  • Does the data help you defend any objections? How?
  • Are there any limitations to the conclusions, any angles you haven’t considered?

If the interpretation of data holds up under these questions and considerations, you have reached a productive conclusion. The only remaining step is to use the process results to decide how you will act.

Join us as we look into the most frequently used question types and how to analyze your findings effectively.

Make the right decisions by analyzing data the right way!

Data analysis advantages

Many industries use data to draw conclusions and decide on actions to implement. It is worth mentioning that science also uses to test or discard existing theories or models.

There’s more than one advantage to data analysis done right. Here are some examples:

data analysis advantages

  • Make faster and more informed business decisions backed by facts.
  • Identify performance issues that require action.
  • Gain a deeper understanding of customer requirements, which creates better business relationships.
  • Increase awareness of risks to implement preventive measures.
  • Visualize different dimensions of the data.
  • Gain competitive advantage.
  • A better understanding of the financial performance of the business.
  • Identify ways to reduce costs and thus increase profits.

These questions are examples of different types of data analysis. You can include them in your post-event surveys aimed at your customers:

  • Questions start with: Why? How? 

Example of qualitative data research analysis: Panels where a discussion is held, and consumers are interviewed about what they like or dislike about the place.

  • Data is collected by asking questions like: How many? Who? How often? Where?

Example of quantitative research analysis: Surveys focused on measuring sales, trends, reports, or perceptions.

Data analysis with QuestionPro

Data analysis is crucial in aiding organizations and individuals in making informed decisions by comprehensively understanding the data. If you’re in need of various data analysis techniques solutions, consider using QuestionPro. Our software allows you to collect data easily, create real-time reports, and analyze data. Practical business intelligence relies on the synergy between analytics and reporting , where analytics uncovers valuable insights, and reporting communicates these findings to stakeholders.

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What is data analysis? Examples and how to get started

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Even with years of professional experience working with data, the term "data analysis" still sets off a panic button in my soul. And yes, when it comes to serious data analysis for your business, you'll eventually want data scientists on your side. But if you're just getting started, no panic attacks are required.

Table of contents:

Quick review: What is data analysis?

Why is data analysis important, types of data analysis (with examples), data analysis process: how to get started, frequently asked questions.

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Data analysis is the process of examining, filtering, adapting, and modeling data to help solve problems. Data analysis helps determine what is and isn't working, so you can make the changes needed to achieve your business goals. 

Keep in mind that data analysis includes analyzing both quantitative data (e.g., profits and sales) and qualitative data (e.g., surveys and case studies) to paint the whole picture. Here are two simple examples (of a nuanced topic) to show you what I mean.

An example of quantitative data analysis is an online jewelry store owner using inventory data to forecast and improve reordering accuracy. The owner looks at their sales from the past six months and sees that, on average, they sold 210 gold pieces and 105 silver pieces per month, but they only had 100 gold pieces and 100 silver pieces in stock. By collecting and analyzing inventory data on these SKUs, they're forecasting to improve reordering accuracy. The next time they order inventory, they order twice as many gold pieces as silver to meet customer demand.

An example of qualitative data analysis is a fitness studio owner collecting customer feedback to improve class offerings. The studio owner sends out an open-ended survey asking customers what types of exercises they enjoy the most. The owner then performs qualitative content analysis to identify the most frequently suggested exercises and incorporates these into future workout classes.

Here's why it's worth implementing data analysis for your business:

Understand your target audience: You might think you know how to best target your audience, but are your assumptions backed by data? Data analysis can help answer questions like, "What demographics define my target audience?" or "What is my audience motivated by?"

Inform decisions: You don't need to toss and turn over a decision when the data points clearly to the answer. For instance, a restaurant could analyze which dishes on the menu are selling the most, helping them decide which ones to keep and which ones to change.

Adjust budgets: Similarly, data analysis can highlight areas in your business that are performing well and are worth investing more in, as well as areas that aren't generating enough revenue and should be cut. For example, a B2B software company might discover their product for enterprises is thriving while their small business solution lags behind. This discovery could prompt them to allocate more budget toward the enterprise product, resulting in better resource utilization.

Identify and solve problems: Let's say a cell phone manufacturer notices data showing a lot of customers returning a certain model. When they investigate, they find that model also happens to have the highest number of crashes. Once they identify and solve the technical issue, they can reduce the number of returns.

There are five main types of data analysis—with increasingly scary-sounding names. Each one serves a different purpose, so take a look to see which makes the most sense for your situation. It's ok if you can't pronounce the one you choose. 

Types of data analysis including text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis.

Text analysis: What is happening?

Text analysis, AKA data mining , involves pulling insights from large amounts of unstructured, text-based data sources : emails, social media, support tickets, reviews, and so on. You would use text analysis when the volume of data is too large to sift through manually. 

Here are a few methods used to perform text analysis, to give you a sense of how it's different from a human reading through the text: 

Word frequency identifies the most frequently used words. For example, a restaurant monitors social media mentions and measures the frequency of positive and negative keywords like "delicious" or "expensive" to determine how customers feel about their experience. 

Language detection indicates the language of text. For example, a global software company may use language detection on support tickets to connect customers with the appropriate agent. 

Keyword extraction automatically identifies the most used terms. For example, instead of sifting through thousands of reviews, a popular brand uses a keyword extractor to summarize the words or phrases that are most relevant. 

Because text analysis is based on words, not numbers, it's a bit more subjective. Words can have multiple meanings, of course, and Gen Z makes things even tougher with constant coinage. Natural language processing (NLP) software will help you get the most accurate text analysis, but it's rarely as objective as numerical analysis. 

Statistical analysis: What happened?

Statistical analysis pulls past data to identify meaningful trends. Two primary categories of statistical analysis exist: descriptive and inferential.

Descriptive analysis

Descriptive analysis looks at numerical data and calculations to determine what happened in a business. Companies use descriptive analysis to determine customer satisfaction , track campaigns, generate reports, and evaluate performance. 

Here are a few methods used to perform descriptive analysis: 

Measures of frequency identify how frequently an event occurs. For example, a popular coffee chain sends out a survey asking customers what their favorite holiday drink is and uses measures of frequency to determine how often a particular drink is selected. 

Measures of central tendency use mean, median, and mode to identify results. For example, a dating app company might use measures of central tendency to determine the average age of its users.

Measures of dispersion measure how data is distributed across a range. For example, HR may use measures of dispersion to determine what salary to offer in a given field. 

Inferential analysis

Inferential analysis uses a sample of data to draw conclusions about a much larger population. This type of analysis is used when the population you're interested in analyzing is very large. 

Here are a few methods used when performing inferential analysis: 

Hypothesis testing identifies which variables impact a particular topic. For example, a business uses hypothesis testing to determine if increased sales were the result of a specific marketing campaign. 

Confidence intervals indicates how accurate an estimate is. For example, a company using market research to survey customers about a new product may want to determine how confident they are that the individuals surveyed make up their target market. 

Regression analysis shows the effect of independent variables on a dependent variable. For example, a rental car company may use regression analysis to determine the relationship between wait times and number of bad reviews. 

Diagnostic analysis: Why did it happen?

Diagnostic analysis, also referred to as root cause analysis, uncovers the causes of certain events or results. 

Here are a few methods used to perform diagnostic analysis: 

Time-series analysis analyzes data collected over a period of time. A retail store may use time-series analysis to determine that sales increase between October and December every year. 

Data drilling uses business intelligence (BI) to show a more detailed view of data. For example, a business owner could use data drilling to see a detailed view of sales by state to determine if certain regions are driving increased sales.

Correlation analysis determines the strength of the relationship between variables. For example, a local ice cream shop may determine that as the temperature in the area rises, so do ice cream sales. 

Predictive analysis: What is likely to happen?

Predictive analysis aims to anticipate future developments and events. By analyzing past data, companies can predict future scenarios and make strategic decisions.  

Here are a few methods used to perform predictive analysis: 

Machine learning uses AI and algorithms to predict outcomes. For example, search engines employ machine learning to recommend products to online shoppers that they are likely to buy based on their browsing history. 

Decision trees map out possible courses of action and outcomes. For example, a business may use a decision tree when deciding whether to downsize or expand. 

Prescriptive analysis: What action should we take?

The highest level of analysis, prescriptive analysis, aims to find the best action plan. Typically, AI tools model different outcomes to predict the best approach. While these tools serve to provide insight, they don't replace human consideration, so always use your human brain before going with the conclusion of your prescriptive analysis. Otherwise, your GPS might drive you into a lake.

Here are a few methods used to perform prescriptive analysis: 

Lead scoring is used in sales departments to assign values to leads based on their perceived interest. For example, a sales team uses lead scoring to rank leads on a scale of 1-100 depending on the actions they take (e.g., opening an email or downloading an eBook). They then prioritize the leads that are most likely to convert. 

Algorithms are used in technology to perform specific tasks. For example, banks use prescriptive algorithms to monitor customers' spending and recommend that they deactivate their credit card if fraud is suspected. 

The actual analysis is just one step in a much bigger process of using data to move your business forward. Here's a quick look at all the steps you need to take to make sure you're making informed decisions. 

Circle chart with data decision, data collection, data cleaning, data analysis, data interpretation, and data visualization.

Data decision

As with almost any project, the first step is to determine what problem you're trying to solve through data analysis. 

Make sure you get specific here. For example, a food delivery service may want to understand why customers are canceling their subscriptions. But to enable the most effective data analysis, they should pose a more targeted question, such as "How can we reduce customer churn without raising costs?" 

These questions will help you determine your KPIs and what type(s) of data analysis you'll conduct , so spend time honing the question—otherwise your analysis won't provide the actionable insights you want.

Data collection

Next, collect the required data from both internal and external sources. 

Internal data comes from within your business (think CRM software, internal reports, and archives), and helps you understand your business and processes.

External data originates from outside of the company (surveys, questionnaires, public data) and helps you understand your industry and your customers. 

You'll rely heavily on software for this part of the process. Your analytics or business dashboard tool, along with reports from any other internal tools like CRMs , will give you the internal data. For external data, you'll use survey apps and other data collection tools to get the information you need.

Data cleaning

Data can be seriously misleading if it's not clean. So before you analyze, make sure you review the data you collected.  Depending on the type of data you have, cleanup will look different, but it might include: 

Removing unnecessary information 

Addressing structural errors like misspellings

Deleting duplicates

Trimming whitespace

Human checking for accuracy 

You can use your spreadsheet's cleanup suggestions to quickly and effectively clean data, but a human review is always important.

Data analysis

Now that you've compiled and cleaned the data, use one or more of the above types of data analysis to find relationships, patterns, and trends. 

Data analysis tools can speed up the data analysis process and remove the risk of inevitable human error. Here are some examples.

Spreadsheets sort, filter, analyze, and visualize data. 

Business intelligence platforms model data and create dashboards. 

Structured query language (SQL) tools manage and extract data in relational databases. 

Data interpretation

After you analyze the data, you'll need to go back to the original question you posed and draw conclusions from your findings. Here are some common pitfalls to avoid:

Correlation vs. causation: Just because two variables are associated doesn't mean they're necessarily related or dependent on one another. 

Confirmation bias: This occurs when you interpret data in a way that confirms your own preconceived notions. To avoid this, have multiple people interpret the data. 

Small sample size: If your sample size is too small or doesn't represent the demographics of your customers, you may get misleading results. If you run into this, consider widening your sample size to give you a more accurate representation. 

Data visualization

Last but not least, visualizing the data in the form of graphs, maps, reports, charts, and dashboards can help you explain your findings to decision-makers and stakeholders. While it's not absolutely necessary, it will help tell the story of your data in a way that everyone in the business can understand and make decisions based on. 

Automate your data collection

Data doesn't live in one place. To make sure data is where it needs to be—and isn't duplicative or conflicting—make sure all your apps talk to each other. Zapier automates the process of moving data from one place to another, so you can focus on the work that matters to move your business forward.

Need a quick summary or still have a few nagging data analysis questions? I'm here for you.

What are the five types of data analysis?

The five types of data analysis are text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Each type offers a unique lens for understanding data: text analysis provides insights into text-based content, statistical analysis focuses on numerical trends, diagnostic analysis looks into problem causes, predictive analysis deals with what may happen in the future, and prescriptive analysis gives actionable recommendations.

What is the data analysis process?

The data analysis process involves data decision, collection, cleaning, analysis, interpretation, and visualization. Every stage comes together to transform raw data into meaningful insights. Decision determines what data to collect, collection gathers the relevant information, cleaning ensures accuracy, analysis uncovers patterns, interpretation assigns meaning, and visualization presents the insights.

What is the main purpose of data analysis?

In business, the main purpose of data analysis is to uncover patterns, trends, and anomalies, and then use that information to make decisions, solve problems, and reach your business goals.

Related reading: 

How to get started with data collection and analytics at your business

How to conduct your own market research survey

Automatically find and match related data across apps

How to build an analysis assistant with ChatGPT

What can the ChatGPT data analysis chatbot do?

This article was originally published in October 2022 and has since been updated with contributions from Cecilia Gillen. The most recent update was in September 2023.

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Shea Stevens

Shea is a content writer currently living in Charlotte, North Carolina. After graduating with a degree in Marketing from East Carolina University, she joined the digital marketing industry focusing on content and social media. In her free time, you can find Shea visiting her local farmers market, attending a country music concert, or planning her next adventure.

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“Data is Everywhere” , in sheets, in social media platforms, in product reviews and feedback, everywhere. In this latest information age it’s created at blinding speeds and, when data is analyzed correctly, can be a company’s most valuable asset. “ To grow your business even to grow in your life, sometimes all you need to do is Analysis! ” If your business is not growing, then you have to look back recognize your mistakes, and make a plan again without repeating those mistakes. And even if your business is growing, then you have to look forward to making the business grow more.

All you need to do is analyze your business data and business processes. The process of studying the data to find out the answers to how and why things happened in the past. Usually, the result of data analysis is the final dataset, i.e. a pattern, or a detailed report that you can further use for Data Analytics . In this article, we will explore What is Data Analysis? , How it works , the types of data analysis , and the Tools required for data analysis .

Table of Content

Why data analysis is important, types of data analysis methods, what is the data analysis process, top data analysis tools, how to become data analyst.

Before jumping into the term “ Data Analysis”, let’s discuss the term “Analysis” . Analysis is a process of answering “How?” and “Why?” . For example, how was the growth of XYZ Company in the last quarter? Or why did the sales of XYZ Company drop last summer? So to answer those questions we take the data that we already have. Out of that, we filter out what we need. This filtered data is the final dataset of the larger chunk that we have already collected and that becomes the target of data analysis . Sometimes we take multiple data sets and analyze them to find a pattern. For example, take summer sales data for three consecutive years. I found out if that fall in sales last summer was because of any specific product that we were selling or if it was just a recurring problem. It’s all about looking for a pattern. We analyze things or events that have already happened in the past.

Let’s say you own a business and sell daily products. Your business model is pretty simple. You buy products from the supplier and sell them to the customer. Let’s assume the biggest challenge for your business is to find the right amount of stock at the given time. You can’t stock excess dairy products as they are perishable and if they go bad you can’t sell them, resulting in a direct loss for you. At the same time, you can not understock as it may result in the loss of potential customers. But data analytics can help you in predicting the strength of your customers at a given time. Using that result, you can sufficiently stock your supplies, in turn, minimizing the loss. In simple words, using data analysis, you can find out the time of the year when your store has the least or the most customers. Using this info, you can stock your supplies accordingly. So these are some reasons why analysis of data is important.

The major Data Analysis methods are:

  • Descriptive Analysis
  • Diagnostic Analysis
  • Predictive Analysis
  • Prescriptive Analysis
  • Statistical Analysis

1. Descriptive Analysis

A Descriptive Analysis looks at data and analyzes past events for insight as to how to approach future events. It looks at the past performance and understands the performance by mining historical data to understand the cause of success or failure in the past. Almost all management reporting such as sales, marketing, operations, and finance uses this type of analysis.

Example: Let’s take the example of DMart, we can look at the product’s history and find out which products have been sold more or which products have large demand by looking at the product sold trends, and based on their analysis we can further make the decision of putting a stock of that item in large quantity for the coming year.

2. Diagnostic Analysis

Diagnostic analysis works hand in hand with Descriptive Analysis . As descriptive Analysis finds out what happened in the past, diagnostic Analysis, on the other hand, finds out why did that happen or what measures were taken at that time, or how frequently it has happened. it basically gives a detailed explanation of a particular scenario by understanding behavior patterns.

Example: Let’s take the example of Dmart again. Now if we want to find out why a particular product has a lot of demand, is it because of their brand or is it because of quality. All this information can easily be identified using diagnostic Analysis.

3. Predictive Analysis

Information we have received from descriptive and diagnostic analysis, we can use that information to predict future data. it basically finds out what is likely to happen in the future. Now when future data doesn’t mean we have become fortune-tellers, by looking at the past trends and behavioral patterns we are forecasting that it might happen in the future.

Example: The best example would be Amazon and Netflix recommender systems. You might have noticed that whenever you buy any product from Amazon, on the payment side it shows you a recommendation saying the customer who purchased this has also purchased this product that recommendation is based on the customer purchase behavior in the past. By looking at customer past purchase behavior analyst creates an association between each product and that’s the reason it shows recommendation when you buy any product.   

4. Prescriptive Analysis

This is an advanced method of Predictive Analysis . Now when you predict something or when you start thinking out of the box you will definitely have a lot of options, and then we get confused as to which option will actually work. Prescriptive Analysis helps to find which is the best option to make it happen or work. As predictive Analysis forecast future data, Prescriptive Analysis on the other hand helps to make it happen whatever we have forecasted. Prescriptive Analysis is the highest level of Analysis that is used for choosing the best optimal solution by looking at descriptive, diagnostic, and predictive data.

Example: The best example would be Google’s self-driving ca r, by looking at the past trends and forecasted data it identifies when to turn or when to slow down, which works much like a human driver.

5. Statistical Analysis

Statistical Analysis is a statistical approach or technique for analyzing data sets in order to summarize their important and main characteristics generally by using some visual aids. This approach can be used to gather knowledge about the following aspects of data:

  • Main characteristics or features of the data.
  • The variables and their relationships.
  • Finding out the important variables that can be used in our problem.

A Data analysis has the ability to transform raw available data into meaningful insights for your business and your decision-making. While there are several different ways of collecting and interpreting this data, most data-analysis processes follow the same six general steps.

  • Specify Data Requirements
  • Collect Data
  • Clean and Process the Data
  • Analyse the Data
  • Interpretation

1. Specify Data Requirements

In step 1 of the data analysis process define what you want to answer through data. This typically stems from a business problem or questions, such as

  • How can we reduce production costs without sacrificing quality?
  • How do customers view our brand?
  • How can we increase sales opportunities using our current resources?

2. Collect Data

  • Find Your Source : Determine what information can be collected from existing sources, and what you need to find elsewhere.
  • Standardize Collection : Create file storage and naming system ahead of time.
  • Keep Track : Keep data organized in a log with dates and add any source notes as you go.

3. Clean and Process the Data

Ensure your data is correct and usable by identifying and removing any errors or corruption.

  • Monitor Errors : Keep a record and look at trends of where most errors are coming from.
  • Validate Accuracy : Research and invest in data tools that allow you to clean your data in real-time.
  • Scrub for Duplicate Data : Identify and remove duplicates so you save time during analysis.
  • Delete all Formatting : Standardise the look of your data by removing any formatting styles.

4. Analyse the Data

Different data analysis techniques allow you to understand, interpret, and derive conclusions based on your business question or problem. 

5. Interpretation

As you interpret the result of your data, ask yourself these key questions:

  • Does the data answer your question? How?
  • Does the data help you defend against any objections? How?
  • Are there any limitations or angles you haven’t considered?

Data Analysis can be used to report to different people:

  • A primary collaborator or client
  • Executive and business leaders
  • A technical supervisor  
  • Keep it Succinct: Organize data in a way that makes it easy for different audiences to skim through it to find the information most relevant to them.
  • Make it Visual: Use data visualizations techniques, such as tables and charts, to communicate the message clearly.
  • Include an Executive Summary : This allows someone to analyze your findings upfront and harness your most important points to influence their decisions.

Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation. Below is the list of some popular tools explain briefly:

  • SAS : SAS was a programming language developed by the SAS Institute for performed advanced analytics, multivariate analyses, business intelligence, data management, and predictive analytics. , SAS was developed for very specific uses and powerful tools are not added every day to the extensive already existing collection thus making it less scalable for certain applications.
  • Microsoft Excel : It is an important spreadsheet application that can be useful for recording expenses, charting data, and performing easy manipulation and lookup and or generating pivot tables to provide the desired summarized reports of large datasets that contain significant data findings. It is written in C# , C++ , and .NET Framework , and its stable version was released in 2016.
  • R  :It is one of the leading programming languages for performing complex statistical computations and graphics. It is a free and open-source language that can be run on various UNIX platforms, Windows, and macOS . It also has a command-line interface that is easy to use. However, it is tough to learn especially for people who do not have prior knowledge about programming.
  • Python :It is a powerful high-level programming language that is used for general-purpose programming . Python supports both structured and functional programming methods. Its extensive collection of libraries make it very useful in data analysis . Knowledge of Tensorflow , Theano , Keras , Matplotlib , Scikit-learn , and Keras can get you a lot closer to your dream of becoming a machine learning engineer.
  • Tableau Public :Tableau Public is free software developed by the public company “ Tableau Software ” that allows users to connect to any spreadsheet or file and create interactive data visualizations. It can also be used to create maps, dashboards along with real-time updation for easy presentation on the web. The results can be shared through social media sites or directly with the client making it very convenient to use.
  • RapidMiner : RapidMiner is an extremely versatile data science platform developed by “RapidMiner Inc”. The software emphasizes lightning-fast data science capabilities and provides an integrated environment for the preparation of data and application of machine learning, deep learning, text mining, and predictive analytical techniques. It can also work with many data source types including Access, SQL , Excel, Tera data, Sybase , Oracle, MySQL , and Dbase.
  • Knime  :Knime, the Konstanz Information Miner is a free and open-source data analytics software. It is also used as a reporting and integration platform. It involves the integration of various components for Machine Learning and data mining through the modular data-pipe lining. It is written in Java and developed by KNIME.com AG . It can be operated in various operating systems such as Linux, OS X, and Windows.

To become a data analyst you must require least a bachelor’s degree. To those who are at higher level , you may require a master’s degree. You also need to developed skills such as : Statistical Analysis , Data Visualization , Data Cleaning , Database Mnagement , and MS-Excel. Start with internships to gain experience and make projects that will demonstrate your skills. The files of Data Analytics is changing rapidly So, you need to keep yourself updated as according to the time by taking online sessions, attending workshops, or reading related books and articles published. As you grow in the field of data science you might find the specific industries to work with and you can explore more in-depth about Data Analysis.

FAQs on Data Analyst?

Q1. how does data analysis differ from data science.

Data Science includes a wider range of activities like create a new algorithm, building predictive models, and harnessing data whereas in Data Analysis includes a performing and processing of datasets that are already existing.

Q2. What tools do data analysts typically use?

The main tools data analysts typically uses that are Excel, SQL , Python , Power BI, R, and Tableau and so on .

Q3. What is the role of a data analyst in a company?

A Data Analyst manage data to help company to make informed decisions. They gather techniques and processes to analyse data . They also communicate their data to stack holders.

Q4. Do I need to Know programming to be a data analyst?

It’s not always compulsory to learn a programming language for this role but languages like Python and R can play a significant role to automate task, and handle large datasets.

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A Step-by-Step Guide to the Data Analysis Process

Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it’s important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.

In this post, we’ll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we’ll also use examples and highlight a few tools to make the journey easier. When you’re done, you’ll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.

Here are the steps we’ll take you through:

  • Defining the question
  • Collecting the data
  • Cleaning the data
  • Analyzing the data
  • Sharing your results
  • Embracing failure

On popular request, we’ve also developed a video based on this article. Scroll further along this article to watch that.

Ready? Let’s get started with step one.

1. Step one: Defining the question

The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’.

Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as: “Why are we losing customers?” It’s possible, though, that this doesn’t get to the core of the problem. A data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.

Let’s say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, “Why are we losing customers?” but, “Which factors are negatively impacting the customer experience?” or better yet: “How can we boost customer retention while minimizing costs?”

Now you’ve defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you’ve noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don’t come back? Which sources of data will help you answer this question?

Tools to help define your objective

Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you’ll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and DashThis . However, you’ll also find open-source software like Grafana , Freeboard , and Dashbuilder . These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.

2. Step two: Collecting the data

Once you’ve established your objective, you’ll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let’s explore each one.

What is first-party data?

First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company’s customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.

What is second-party data?

To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.

What is third-party data?

Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data .

Tools to help you collect data

Once you’ve devised a data strategy (i.e. you’ve identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you’ll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP , SAS , and the data integration platform, Xplenty . If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm .

Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics .

3. Step three: Cleaning the data

Once you’ve collected your data, the next step is to get it ready for analysis. This means cleaning, or ‘scrubbing’ it, and is crucial in making sure that you’re working with high-quality data . Key data cleaning tasks include:

  • Removing major errors, duplicates, and outliers —all of which are inevitable problems when aggregating data from numerous sources.
  • Removing unwanted data points —extracting irrelevant observations that have no bearing on your intended analysis.
  • Bringing structure to your data —general ‘housekeeping’, i.e. fixing typos or layout issues, which will help you map and manipulate your data more easily.
  • Filling in major gaps —as you’re tidying up, you might notice that important data are missing. Once you’ve identified gaps, you can go about filling them.

A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one…so don’t rush it! You’ll find a step-by-step guide to data cleaning here . You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.

Carrying out an exploratory analysis

Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let’s use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning’s clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.

Tools to help you clean your data

Cleaning datasets manually—especially large ones—can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine , are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder , which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?

4. Step four: Analyzing the data

Finally, you’ve cleaned your data. Now comes the fun bit—analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.

Descriptive analysis

Descriptive analysis identifies what has already happened . It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let’s refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they’ll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.

Learn more: What is descriptive analytics?

Diagnostic analysis

Diagnostic analytics focuses on understanding why something has happened . It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. Remember TopNotch Learning’s business problem? ‘Which factors are negatively impacting the customer experience?’ A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let’s imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they’re losing customers because they lack expertise in this sector. And that’s a useful insight!

Predictive analysis

Predictive analysis allows you to identify future trends based on historical data . In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn’t stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they’ll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.

Prescriptive analysis

Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It’s also the most complex. This is because it incorporates aspects of all the other analyses we’ve described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.

Learn more:  What are the different types of data analysis?

5. Step five: Sharing your results

You’ve finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization’s stakeholders!) This is more complex than simply sharing the raw results of your work—it involves interpreting the outcomes, and presenting them in a manner that’s digestible for all types of audiences. Since you’ll often present information to decision-makers, it’s very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.

How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That’s why it’s very important to provide all the evidence that you’ve gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it’s important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!

Tools for interpreting and sharing your findings

There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts , Tableau , Datawrapper , and Infogram . If you’re familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly , Seaborn , and Matplotlib . Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!

You can learn more about storytelling with data in this free, hands-on tutorial .  We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it’s really like to work as a data analyst!

6. Step six: Embrace your failures

The last ‘step’ in the data analytics process is to embrace your failures. The path we’ve described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you’d never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.

While these pitfalls can feel like failures, don’t be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What’s important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn’t be as interesting. Use the steps we’ve outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.

In this post, we’ve covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst’s work:

  • Define the question —What business problem are you trying to solve? Frame it as a question to help you focus on finding a clear answer.
  • Collect data —Create a strategy for collecting data. Which data sources are most likely to help you solve your business problem?
  • Clean the data —Explore, scrub, tidy, de-dupe, and structure your data as needed. Do whatever you have to! But don’t rush…take your time!
  • Analyze the data —Carry out various analyses to obtain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
  • Share your results —How best can you share your insights and recommendations? A combination of visualization tools and communication is key.
  • Embrace your mistakes —Mistakes happen. Learn from them. This is what transforms a good data analyst into a great one.

What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we’ve described, you can create a tailored technique that works for you.

To learn more, check out our free, 5-day data analytics short course . You might also be interested in the following:

  • These are the top 9 data analytics tools
  • 10 great places to find free datasets for your next project
  • How to build a data analytics portfolio

Guru99

What is Data Analysis? Research, Types & Example

Daniel Johnson

What is Data Analysis?

Data analysis is defined as a process of cleaning, transforming, and modeling data to discover useful information for business decision-making. The purpose of Data Analysis is to extract useful information from data and taking the decision based upon the data analysis.

A simple example of Data analysis is whenever we take any decision in our day-to-day life is by thinking about what happened last time or what will happen by choosing that particular decision. This is nothing but analyzing our past or future and making decisions based on it. For that, we gather memories of our past or dreams of our future. So that is nothing but data analysis. Now same thing analyst does for business purposes, is called Data Analysis.

In this Data Science Tutorial, you will learn:

Why Data Analysis?

To grow your business even to grow in your life, sometimes all you need to do is Analysis!

If your business is not growing, then you have to look back and acknowledge your mistakes and make a plan again without repeating those mistakes. And even if your business is growing, then you have to look forward to making the business to grow more. All you need to do is analyze your business data and business processes.

Data Analysis Tools

Data Analysis Tools

Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and it also helps to identify patterns and trends for interpretation. Here is a complete list of tools used for data analysis in research.

Types of Data Analysis: Techniques and Methods

There are several types of Data Analysis techniques that exist based on business and technology. However, the major Data Analysis methods are:

Text Analysis

Statistical analysis, diagnostic analysis, predictive analysis, prescriptive analysis.

Text Analysis is also referred to as Data Mining. It is one of the methods of data analysis to discover a pattern in large data sets using databases or data mining tools . It used to transform raw data into business information. Business Intelligence tools are present in the market which is used to take strategic business decisions. Overall it offers a way to extract and examine data and deriving patterns and finally interpretation of the data.

Statistical Analysis shows “What happen?” by using past data in the form of dashboards. Statistical Analysis includes collection, Analysis, interpretation, presentation, and modeling of data. It analyses a set of data or a sample of data. There are two categories of this type of Analysis – Descriptive Analysis and Inferential Analysis.

Descriptive Analysis

analyses complete data or a sample of summarized numerical data. It shows mean and deviation for continuous data whereas percentage and frequency for categorical data.

Inferential Analysis

analyses sample from complete data. In this type of Analysis, you can find different conclusions from the same data by selecting different samples.

Diagnostic Analysis shows “Why did it happen?” by finding the cause from the insight found in Statistical Analysis. This Analysis is useful to identify behavior patterns of data. If a new problem arrives in your business process, then you can look into this Analysis to find similar patterns of that problem. And it may have chances to use similar prescriptions for the new problems.

Predictive Analysis shows “what is likely to happen” by using previous data. The simplest data analysis example is like if last year I bought two dresses based on my savings and if this year my salary is increasing double then I can buy four dresses. But of course it’s not easy like this because you have to think about other circumstances like chances of prices of clothes is increased this year or maybe instead of dresses you want to buy a new bike, or you need to buy a house!

So here, this Analysis makes predictions about future outcomes based on current or past data. Forecasting is just an estimate. Its accuracy is based on how much detailed information you have and how much you dig in it.

Prescriptive Analysis combines the insight from all previous Analysis to determine which action to take in a current problem or decision. Most data-driven companies are utilizing Prescriptive Analysis because predictive and descriptive Analysis are not enough to improve data performance. Based on current situations and problems, they analyze the data and make decisions.

Data Analysis Process

The Data Analysis Process is nothing but gathering information by using a proper application or tool which allows you to explore the data and find a pattern in it. Based on that information and data, you can make decisions, or you can get ultimate conclusions.

Data Analysis consists of the following phases:

Data Requirement Gathering

Data collection, data cleaning, data analysis, data interpretation, data visualization.

First of all, you have to think about why do you want to do this data analysis? All you need to find out the purpose or aim of doing the Analysis of data. You have to decide which type of data analysis you wanted to do! In this phase, you have to decide what to analyze and how to measure it, you have to understand why you are investigating and what measures you have to use to do this Analysis.

After requirement gathering, you will get a clear idea about what things you have to measure and what should be your findings. Now it’s time to collect your data based on requirements. Once you collect your data, remember that the collected data must be processed or organized for Analysis. As you collected data from various sources, you must have to keep a log with a collection date and source of the data.

Now whatever data is collected may not be useful or irrelevant to your aim of Analysis, hence it should be cleaned. The data which is collected may contain duplicate records, white spaces or errors. The data should be cleaned and error free. This phase must be done before Analysis because based on data cleaning, your output of Analysis will be closer to your expected outcome.

Once the data is collected, cleaned, and processed, it is ready for Analysis. As you manipulate data, you may find you have the exact information you need, or you might need to collect more data. During this phase, you can use data analysis tools and software which will help you to understand, interpret, and derive conclusions based on the requirements.

After analyzing your data, it’s finally time to interpret your results. You can choose the way to express or communicate your data analysis either you can use simply in words or maybe a table or chart. Then use the results of your data analysis process to decide your best course of action.

Data visualization is very common in your day to day life; they often appear in the form of charts and graphs. In other words, data shown graphically so that it will be easier for the human brain to understand and process it. Data visualization often used to discover unknown facts and trends. By observing relationships and comparing datasets, you can find a way to find out meaningful information.

  • Data analysis means a process of cleaning, transforming and modeling data to discover useful information for business decision-making
  • Types of Data Analysis are Text, Statistical, Diagnostic, Predictive, Prescriptive Analysis
  • Data Analysis consists of Data Requirement Gathering, Data Collection, Data Cleaning, Data Analysis, Data Interpretation, Data Visualization
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Data Analyst

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what is analysis data

What Is a Data Analyst? How to Become One, Salary, Skills.

Data analysts solve measurable business problems with the help of computer programming and data analytics practices. Here’s what to know about a data analyst’s needed skills, salary and how to become one.

What Is a Data Analyst?

Data analysts conduct statistical analysis on structured data to uncover relevant business conclusions. They primarily focus on converting tangible, readily available data into actionable insights and answers.

What Do Data Analysts Do?

Data analysts acquire and organize cleaned data to search for applicable patterns and trends. They utilize data analysis techniques, programming languages and data visualization tools to conduct analysis and display their findings. Unlike data scientists , data analysts usually don’t work with raw data or machine learning models and don’t conduct largely hypothetical analysis. 

Data Analyst Responsibilities

  • Gather, reorganize and clean data as necessary from primary and secondary sources.
  • Analyze and interpret patterns and trends in structured data sets.
  • Extract actionable business insights and present findings to other professionals.
  • Communicate with various parties to identify data information needs.

Day-to-Day Responsibilities of Data Analysts

  • Use analytics platforms like KNIME to aggregate and clean collected data.
  • Use programming languages like Python to manage data structures and conduct data mining operations.
  • Create conclusion charts and graphs with visualization tools like Tableau.
  • Collaborate with software developers to optimize data collection and analysis systems.

Data Analysts Within a Company

Data analysts are usually part of a data science team within a company.  They frequently collaborate with business intelligence analysts , data engineers , data scientists and software developers to accomplish their work.

Importance of Data Analysts

The insights that data analysts uncover through their work can be used to alleviate workflow roadblocks or to eventually make impactful business decisions. Specific business problems or optimization issues that relate to data may take longer to solve without data analysts.

What Skills Are Needed to Be a Data Analyst?

Qualifications to be a data analyst.

  • Internship and/or on-the-job training experience in data science or data analytics.
  • Ability to organize, clean and interpret large sets of data.
  • Ability to conduct statistical and regression analysis to track and identify trends.
  • Proficiency in Python or R for data analysis purposes.

Data Analyst Prerequisites

  • Bachelor’s degree in computer science, information systems, statistics or a similar field.

Data Analyst Hard Skills

  • Expertise in data analysis, cleaning and preparation.
  • Knowledge of big data tools and databases.
  • Knowledge of cloud computing technologies.
  • Experience with data analysis tools and techniques.
  • Experience with data visualization tools.
  • Knowledge of machine learning technologies.
  • Expertise in programming languages (Java, Python, R, Scala, SQL)
  • Experience with statistics, mathematics and related analysis.

Data Analyst Soft Skills

  • Collaboration. 
  • Critical thinking skills.
  • Problem-solving skills.
  • Verbal and written communication skills.

Tools and Programs Data Analysts Use

  • Google Sheets
  • Jupyter Notebook
  • Microsoft Excel
  • Microsoft Power BI 

How to Become a Data Analyst

Data analyst education and experience.

Data analyst candidates are often expected to have a bachelor’s degree in computer science, information systems, statistics or a similar field. 

Candidates will often need to obtain applicable data science or analytics experience through an internship, on-the-job training and/or work experience. Knowledge in the areas of data analysis and tools, data visualization, programming languages (Java, Python, R, Scala, SQL), statistics, big data and effective communication are also recommended.

Data Analyst Certificates and Courses

  • 21 Python Data Science Courses and Bootcamps to Know
  • Data Analytics Accelerator
  • Data Analytics Bootcamp
  • Intro to Data Analytics Webinar
  • Learning Python for Data Analysis and Visualization

Data Analyst Career Path

After gaining experience as a data analyst, professionals can move into a data scientist, data analytics consultant or specialist role like marketing analyst, operations analyst or systems analyst. From here, professionals may progress into management and leadership roles like senior data analyst, analytics manager, director of analytics or chief data officer.

Data Analyst Salary and Job Outlook

Data analysts jobs, falling under the category of operations research analyst jobs by the U.S. Bureau of Labor Statistics, are projected to grow 23 percent by 2031.

The full compensation package for a data analyst depends on a variety of factors, including but not limited to the candidate’s experience and geographic location. See below for detailed information on the average data analyst salary.

Expand Your Data Analyst Career Opportunities

Invest in your skillset by taking expert-led data science courses from Udemy.

what is analysis data

Regardless of your industry or role, fluency in the language of data analytics will allow you to contribute to data driven decision making.

Hello and welcome to the course  Product Management: Business KPIs & User Metrics analysis.

In this course, you will learn

#1 - How to look at user metrics & business KPIs

PREDICTIVE, PRESCRIPTIVE ANALYTICS FOR BUSINESS DECISION MAKING

LEARN HOW TO BUILD PREDICTIVE AND PRESCRIPTIVE MODELS USING NUMERICAL DATA

Prescriptive analytics can cut through the clutter of…

Hello, My name is  Minerva Singh  and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation).

I have several…

Data Analyst Certifications + Programs

Give your resume a boost with in-demand data science certifications available from Udacity.

what is analysis data

Whether you have coded before or are brand new to the world of programming, this course will put you on the fast track to building confidence with this intuitive, object- oriented language. Learn programming fundamentals and build a custom application. Graduate with the ability to start applying Python within high-growth fields like analytics, data science, and web development. 

What you'll accomplish

This is a beginner-friendly program with no prerequisites, although some students may have coded previously. First-time programmers will have access to pre-course preparatory lessons and additional resources to boost their confidence with key concepts and set up their development environments. Throughout this expert-designed program, you’ll:

  • Learn object-oriented programming fundamentals and Python basics that get you coding from day one.
  • Build a Python program and add on increased complexity throughout the course.
  • Troubleshoot Python code and practice common debugging techniques.
  • Push your skills to the next level by adding scripting, modules, and APIs to your Python toolkit.
  • Explore introductory data science and web development as potential career directions for Python programmers.
  • Demonstrate your Python skills by creating apps that pull in data with Pandas or integrate functionality from APIs with Flask.

Why General Assembly

Since 2011, General Assembly has graduated more than 40,000 students worldwide from the full time & part time courses. During the 2020 hiring shutdown, GA's students, instructors, and career coaches never lost focus, and the KPMG-validated numbers in their Outcomes report reflect it. *For students who graduated in 2020 — the peak of the pandemic — 74.4% of those who participated in GA's full-time Career Services program landed jobs within six months of graduation. General Assembly is proud of their grads + teams' relentless dedication and to see those numbers rising. Download the report here .

Your next step? Submit an application to talk to the General Assembly Admissions team

Note: reviews are referenced from Career Karma - https://careerkarma.com/schools/general-assembly

what is analysis data

General Assembly’s Data Analytics Immersive is designed for you to harness Excel, SQL, and Tableau to tell compelling stories with a data driven strategy. This program was created for analysts, digital marketers, sales managers, product managers, and data novices looking to learn the essentials of data analysis. 

You will learn to use industry tools, Excel, and SQL to analyze large real world data sets and create data dashboards and visualizations to share your findings. The Data Analytics Accelerator culminates in a.

Throughout this expert-designed program, you’ll:

  • Use Excel, SQL, and Tableau to collect, clean, and analyze large data sets.
  • Present data-driven insights to key stakeholders using data visualization and dashboards.
  • Tell compelling stories with your data.
  • Graduate with a professional portfolio of projects that includes a capstone project applying rigorous data analysis techniques to solve a real-world problem

what is analysis data

General Assembly’s Data Analytics Immersive is a transformative course designed for you to get the necessary skills for a data analyst role in three months. 

The Data Analytics bootcamp is led by instructors who are expert practitioners in their field, supported by career coaches that work with you since day one and enhanced by a career services team that is constantly in talks with employers about their tech hiring needs.

As a graduate, you’ll have a portfolio of projects that show your knowledge of data analytics skills, as well as experience with visualization tools and frameworks that employers demand. Throughout this expert-designed program, you’ll:

  • Acquire, analyze, and visualize data sets in real time.
  • Master industry-standard tools like SQL, Excel, Tableau, PowerBI, and Python.
  • Turn data into stories that can influence and inform important decisions.
  • Ask the right questions and answer them with data-informed insights.
  • Demonstrate what you’ve learned with a solid professional portfolio.

Note: reviews are referenced from Career Karma - https://careerkarma.com/schools/general-assembl

Careers Related to Data Analyst

Data analyst jobs, companies hiring data analysts, most common skills for data analyst, related data science careers.

Watch CBS News

Fulton County D.A.'s office disputes new Trump claims about Fani Willis' relationship with her deputy Nathan Wade

By Daniel Klaidman

Updated on: February 24, 2024 / 1:41 AM EST / CBS News

The Fulton County District Attorney's office filed a response Friday to an analysis filed by former President Donald Trump's lawyers of phone records that purport to raise questions about the timeline D.A. Fani Willis gave regarding when her relationship with special prosecutor Nathan Wade began. 

Trump's attorneys filed an analysis Friday of cellphone data that allegedly belongs to Wade as part of their motion to disqualify D.A. Fani Willis and her office from prosecuting the Trump 2020 Georgia election interference case . The defense team seeks to use the data to show that Wade was at the condominium where Willis was living late at night and well into the early morning hours on Sept. 11 and Sept. 12, 2021, and Nov. 29 and Nov. 30 of that same year. Willis acknowledged the romantic relationship in court papers, and both Wade and Willis testified under oath last week that their romantic relationship did not begin until early 2022, after Willis hired Wade to work on the Georgia election case. 

A source close to Willis said lawyers in her office are trying to challenge the interpretation of the data filed by Trump's lawyers. If the data analysis is proven to be accurate, it might serve as powerful evidence that Wade and Willis misled the court about when their relationship began.  

In Willis' filing, the district attorney objected to the defense's document and requested that the court exclude the data, arguing that it "contains both 2 telephone records that have not been admitted into evidence and an affidavit and other documents containing unqualified opinion evidence." Willis said in the filing that Trump had not provided enough written notice to the court or a summary of its "purported expert's testimony" and offered no information on the witness' qualifications to serve as an expert witness.  

Willis also asserted that the phone records themselves had not been authenticated and contended that the records "do not prove, in any way, the content of the communications between Special Prosecutor Wade and District Attorney Willis." They don't prove that the two "were ever in the same place during any of the times listed," she wrote, and she also stated that "on multiple relevant dates and times, evidence clearly demonstrates that District Attorney Willis was elsewhere, including at work at the Fulton County District Attorney's Office" and visiting "three crime scenes." 

The Trump team's analysis was conducted by a private investigator who used a geofencing analytics tool called CellHawk, which the investigator called the "gold standard" in cellphone records analysis. 

Defense lawyers in the case have been trying to prove that Willis entered into a corrupt bargain with Wade during their romantic relationship to place him on the prosecution team, pay him hundreds of thousands of dollars, so that the two of them could benefit financially. 

On Friday, lawyers for the D.A.'s office were trying to find their own expert witnesses who would be able to counter what the Trump lawyers have asserted, according to the source. Their hope is to be able to file a response as early as Friday or this weekend.  

"The interpretation of the data is not what you think it is," the source told CBS News. 

Lawyers for the D.A.'s office are not expected to claim that Wade did not visit the condo on multiple occasions — indeed both Wade and Willis have testified that Willis was there as many as 10 times — but they will maintain that the relationship had not developed into a romance during that period.  

The source also says that it was not uncommon for Willis to hold work meetings at the condo. Wade wasn't working in the D.A.'s office until November of 2021, but the source says he was part of her "kitchen cabinet" before that. In one meeting, according to the source, Wade was involved in discussions about the Atlanta spa shooting case in which eight people were allegedly shot to death by a disturbed 21-year old gunman. 

At the end of the two-day hearing last week, Fulton County Judge Scott McAfee questioned defense lawyers on whether they had any other witnesses or evidence they planned to introduce. Only Trump's lawyer, Steven Sadow, did. He informed McAfee that the defense had obtained cellphone records that he wanted to make part of the record. The records, he indicated, related to his questioning of Wade about visits to the condo and said they dealt with the period between February and November 2021.  

"Based on our preliminary research," Sadow said, "we'd like to re-open and be able to introduce the records and someone to explain what they mean." 

Judge McAfee was noncommittal. But should the D.A.'s office further respond with evidence that counters the cellphone data introduced by Sadow, McAfee could extend the evidentiary hearing, rather than proceed immediately to closing arguments, which are currently scheduled for March 1.

Should that happen, the two sides will argue over the reliability of the data and whose interpretation is correct. But the critical question, which could determine Willis' fate in the case, is whether the judge believes the Fulton County D.A. misled the court, legal experts say.  

Willis' most ardent defenders are continuing to rally to her side. In an interview with CBS News, Norman Eisen, a lawyer and former Obama administration ethics czar, questioned the reliability of cellphone data and the defense tactics.  

"This kind of data is unreliable, as shown by a similar failed effort in the 2020 Mules case," Eisen said, referring to a case that involved false claims made by election deniers in 2020 to use drop boxes to commit voter fraud. Moreover, Eisen said that the issue should have been raised earlier "when it could have been tested," by Willis' lawyers. Still, Eisen acknowledged that "as a general matter judges always care about the honesty of those appearing before them," before noting that in this case the defense is "attempting to cross a bridge too far – and one that rests on shaky foundations."    

  • Donald Trump
  • Fani Willis

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What is business analytics?

Man displays data on a screen to a conference room of individuals.

It should come as no surprise that big business decisions are made every single day at companies small and large. 

It is also well assumed that the best big decisions are ones with evidence and back them up—in the form of data. But how does data go from being raw information like surveys and click-through rates to being part of sometimes world-altering decision-making? Business analytics is how.

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The Online MS in Business Analytics from Pepperdine

The emphasis on data-powered decision-making is nothing new; in fact, businesses have known about its significance for years. A decade ago, Deloitte noted in a 2013 study that focus on big data and analytics were to be the “new normal” for maintaining growth. “Companies must focus on evolving their analytical maturity in addition to developing capabilities around rapid experimentation and trial and error. Remaining agile will be essential for handling this “new normal,” it stated.

So, with today there being hundreds of thousands of workers who describe themselves as business analysts (not to mention there now being an entire international organization dedicated to the field, the IIBA ), an important question lingers: what exactly even is business analytics? Fortune has you covered.

In the simplest terms, business analytics is the process or the ability to drive decisions using data and analytics, according to Anindya Ghose, the director of the master’s of science in business analytics program at New York University’s Stern School of Business. The school Stern is home to the no. 9 best MBA program , based on Fortune ’s ranking.

Business analytics is a field that is constantly evolving in accordance with technological developments. A few decades ago, business analytics was a much simpler domain in the typical business-tech space: spreadsheets could house information, trends could be identified using basic formulas, and data could be visualized to the team of decision-makers.

But today, business analytics is everywhere—in tech, healthcare, education, retail, media, and beyond. 

“The way we think about business analytics now—it’s a little bit of everything for everybody,” says Devanshu Mehrotra, curriculum developer and lead instructor at General Assembly, with a background in the world of analytics.

Business analytics is more so the art of data translating, says Mehrotra.

“And the idea is, since data is being democratized, and the idea is that specific organizations should own their data, they should be responsible for their data, then it’s important for there to be data translators,” he adds.

What skills do you need for business analytics?

While the exact skills needed to excel in business analytics may differ depending on industry, company, and level of experience, there are several foundations that are important to have, including:

  • Domain expertise: business fundamentals and relevant industry knowledge
  • Technical know-how: programming, data analysis, data visualization
  • Storytelling: translating data trends to business needs

The last point in particular was something Mehrotra and Ghose both emphasized as an area that really sets excellent business analysts apart from other fields. 

Additionally, knowledge of both high and low code tools are important technical aspects of the job, including for, as Mehrotra notes:

Because there are many data-related tools available—and every company may use something different—Mehrotra says it is important to be tool agnostic. 

“Multiple tools should be in your repertoire, (so) that you pick the tool based on the problem, not try and shove every problem into the two tools that you know,” he says. “And that’s why I’m always like—it’s do you understand the why before you understand the how.”

Ghose adds that in order to succeed in business analytics, having training in these two areas are of great importance:

  • Econometrics (advanced statistics and modeling)
  • Experimental design (creation and understanding of tests and behaviors)

It would also be remiss to not mention the criticality of AI in space. Like other fields, the tech is streamlining some of the day-to-day activities of business analytics. 

How can you learn business analytics?

Those wanting to get involved in business analytics are in luck because there are numerous ways to learn the in-demand skills.

When looking at traditional degree pathways, many universities have undergraduate and graduate degrees focused specifically on business analytics. ( Fortune ranks the best online master’s in business analytics ). And even if there is no program labeled business analytics directly, you can also gain through a combination of business and data science endeavors.

If a longer degree program is not for you, checking out a bootcamp or course in business analytics may provide a quicker, cheaper, and/or more flexible opportunity.

A few years ago, Mehrotra explains he may have recommended going down a traditional degree route, but because the world of analytics is always changing, a shorter program may be a better way to get the most up-to-date skills from instructors with recent industry experience.

“To me, I think long form education, specifically around these areas are not very impactful and not a good return on investment,” Mehrotra says. “I think short form and creating your own journey, so as to speak, is important and I do think that some kind of short form educational programs are a very important part of that.”

Regardless, what’s key to sticking out in a competitive job ecosystem is gaining hands-on projects, creating a portfolio, and learning from instructors with real-world experience, Mehrotra notes.

Studying business analytics also does not necessarily mean you are boxed in to becoming a business analyst. Other job titles may include data scientist , data analyst , market researcher, chief digital officer, chief data officer, head of product, and intelligence analyst.

“It’s now increasingly difficult, if not impossible to imagine—taking decisions without the help of computers, algorithms and data,” Ghose says. “So, you will almost certainly see lots of benefits from that. I think that is just the way of the world today will just continue to be even more ubiquitous as we proceed. So, jump in and join the party.”

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Harvard Business Analytics Program

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Regions & Countries

Race and lgbtq issues in k-12 schools, what teachers, teens and the u.s. public say about current curriculum debates.

Demonstrators outside a school board meeting in Glendale, California, on June 20, 2023. (David McNew/Getty Images)

Pew Research Center conducted this study to better understand how public K-12 teachers, teens and the American public see topics related to race, sexual orientation and gender identity playing out in the classroom.

The bulk of the analysis in this report is based on an online survey of 2,531 U.S. public K-12 teachers conducted from Oct. 17 to Nov. 14, 2023. The teachers surveyed are members of RAND’s American Teacher Panel, a nationally representative panel of public school K-12 teachers recruited through MDR Education. Survey data is weighted to state and national teacher characteristics to account for differences in sampling and response to ensure they are representative of the target population.

For the questions for the general public, we surveyed 5,029 U.S. adults from Nov. 9 to Nov. 16, 2023. The adults surveyed are members of the Ipsos KnowledgePanel, a nationally representative online survey panel. Panel members are randomly recruited through probability-based sampling, and households are provided with access to the Internet and hardware if needed. To ensure that the results of this survey reflect a balanced cross section of the nation, the data is weighted to match the U.S. adult population by gender, age, education, race and ethnicity and other categories.

For questions for teens, we conducted an online survey of 1,453 U.S. teens from Sept. 26 to Oct. 23, 2023, through Ipsos. Ipsos recruited the teens via their parents, who were part of its KnowledgePanel. The survey was weighted to be representative of U.S. teens ages 13 to 17 who live with their parents by age, gender, race and ethnicity, household income, and other categories. The survey on teens was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants.

Here are the questions used for this report , along with responses, and the survey methodology .

Throughout the report, references to White, Black and Asian adults include those who are not Hispanic and identify as only one race. Hispanics are of any race. The views and experiences of teachers and teens who are Asian American or part of other racial and ethnic groups are not analyzed separately in this report due to sample limitations. Data for these groups is incorporated into the general population figures throughout the report.

All references to party affiliation include those who lean toward that party. Republicans include those who identify as Republicans and those who say they lean toward the Republican Party. Democrats include those who identify as Democrats and those who say they lean toward the Democratic Party.

Political leaning of school districts is based on whether the majority of those residing in the school district voted for Republican Donald Trump or Democrat Joe Biden in the 2020 presidential election.

Amid national debates about what schools are teaching , we asked public K-12 teachers, teens and the American public how they see topics related to race, sexual orientation and gender identity playing out in the classroom.

A pie chart showing that about 4 in 10 teachers say current debates about K-12 education have had a negative impact on their job.

A sizeable share of teachers (41%) say these debates have had a negative impact on their ability to do their job. Just 4% say these debates have had a positive impact, while 53% say the impact has been neither positive nor negative or that these debates have had no impact.

And 71% of teachers say teachers themselves don’t have enough influence over what’s taught in public schools in their area.

In turn, a majority of teachers (58%) say their state government has too much influence over this. And more say the federal government, the local school board and parents have too much influence than say they don’t have enough.

Most of the findings in this report come from a survey of 2,531 U.S. public K-12 teachers conducted Oct. 17-Nov. 14, 2023, using the RAND American Teacher Panel. 1 The survey looks at teachers’ views on:

  • Race and LGBTQ issues in the classroom ( Chapter 1 )
  • Current debates over what schools should be teaching and the role of key groups ( Chapter 2 )

It follows a fall 2022 survey of K-12 parents that explored similar topics.

This report also includes some findings from a survey of U.S. teens ages 13 to 17 ( Chapter 3 ) and a survey of U.S. adults ( Chapter 4 ). For details about these surveys, refer to the Methodology section of this report. Among the key findings:

  • 38% of teens say they feel comfortable when topics related to racism or racial inequality come up in class (among those who say these topics have come up). A smaller share (29%) say they feel comfortable when topics related to sexual orientation or gender identity come up.
  • Among the American public , more say parents should be able to opt their children out of learning about LGBTQ issues than say the same about topics related to race (54% vs. 34%).

What do teachers think students should learn about slavery and gender identity?

A diverging bar chart showing that most teachers think students should learn that the legacy of slavery still affects Black Americans today.

We asked public K-12 teachers what they think students should learn in school about two topics in particular:

  • Whether the legacy of slavery still affects the position of Black people in American society today.
  • Whether a person’s gender can be different from or is determined by their sex at birth.

For these questions, elementary, middle and high school teachers were asked about elementary, middle and high school students, respectively.

The legacy of slavery

Most teachers (64%) say students should learn that the legacy of slavery still affects the position of Black people in American society today.

About a quarter (23%) say students should learn that slavery is part of American history but no longer affects the position of Black people in American society. Just 8% say students shouldn’t learn about this topic in school at all.

Majorities of elementary, middle and high school teachers say students should learn that the legacy of slavery still has an impact on the lives of Black Americans.

Gender identity

A diverging bar chart showing that most elementary school teachers say students shouldn’t learn about gender identity at school.

When it comes to teaching about gender identity – specifically whether a person’s gender can be different from or is determined by their sex assigned at birth – half of public K-12 teachers say students shouldn’t learn about this in school.

A third of teachers think students should learn that someone can be a boy or a girl even if that is different from the sex they were assigned at birth.

A smaller share (14%) say students should learn that whether someone is a boy or a girl is determined by their sex at birth.

Views differ among elementary, middle and high school teachers. But teachers across the three levels are more likely to say students should learn that a person’s gender can be different from their sex at birth than to say students should learn gender is determined by sex at birth.

Most elementary school teachers (62%) say students shouldn’t learn about gender identity in school. This is much larger than the shares of middle and high school teachers who say the same (45% and 35%).

What parents and teens say

Parents of K-12 students are more divided on what their children should learn in school about these topics.

In the 2022 survey , 49% of parents said they’d rather their children learn that the legacy of slavery still affects the position of Black people in American society today, while 42% said they’d rather their children learn that slavery no longer affects Black Americans.

When it comes to gender identity, 31% of parents said they’d rather their children learn that gender can be different from sex at birth. An identical share said they would rather their children learn gender is determined by sex at birth. Another 37% of parents said their children shouldn’t learn about gender identity in school.

Teens, like parents, are more divided than teachers on these questions. About half of teens (48%) say they’d rather learn that the legacy of slavery still affects the position of Black Americans today. Four-in-ten would prefer to learn that slavery no longer affects Black Americans.

And teens are about evenly divided when it comes to what they prefer to learn about gender identity. A quarter say they’d rather learn that a person’s gender can be different from their sex at birth; 26% would prefer to learn that gender is determined by sex at birth. About half (48%) say they shouldn’t learn about gender identity in school.

For more on teens’ views about what they prefer to learn in school about each of these topics, read Chapter 3 of this report.

Should parents be able to opt their children out of learning about certain topics?

Most public K-12 teachers (60%) say parents should not be able to opt their children out of learning about racism or racial inequality in school, even if the way these topics are taught conflicts with the parents’ beliefs. A quarter say parents should be able to opt their children out of learning about these topics.

In contrast, more say parents should be able to opt their children out of learning about sexual orientation or gender identity (48%) than say parents should not be able to do this (33%).

On topics related to both race and LGBTQ issues, elementary and middle school teachers are more likely than high school teachers to say parents should be able to opt their children out.

How teachers’ views compare with the public’s views

A diverging bar chart showing that 54% of Americans say parents should be able to opt their children out of learning about LGBTQ issues.

Like teachers, Americans overall are more likely to say parents should be able to opt their children out of learning about sexual orientation or gender identity (54%) than to say they should be able to opt their children out of learning about racism or racial inequality (34%).

Across both issues, Americans overall are somewhat more likely than teachers to say parents should be able to opt their children out.

For more on the public’s views, read Chapter 4 of this report.

How often do topics related to race and LGBTQ issues come up in the classroom?

A horizontal stacked bar chart showing that topics related to racism and racial inequality come up in the classroom more often than LGBTQ issues.

Most teachers who’ve been teaching for more than a year (68%) say the topics of sexual orientation and gender identity rarely or never came up in their classroom in the 2022-23 school year. About one-in-five (21%) say these topics came up sometimes, and 8% say they came up often or extremely often.

Topics related to racism or racial inequality come up more frequently. A majority of teachers (56%) say these topics came up at least sometimes in their classroom, with 21% saying they came up often or extremely often.

These topics are more likely to come up in secondary school than in elementary school classrooms.

How do teachers’ views differ by party?

As is the case among parents of K-12 students and the general public, teachers’ views on how topics related to race and LGBTQ issues should play out in the classroom differ by political affiliation.

  • What students should learn about slavery: 85% of Democratic and Democratic-leaning teachers say students should learn that the legacy of slavery still affects the position of Black people in American society today. This compares with 35% of Republican and Republican-leaning teachers who say the same.

A diverging bar chart showing that teachers’ views on parents opting their children out of learning about race, LGBTQ issues differ widely by party.

  • What students should learn about gender identity: Democratic teachers are far more likely than Republican teachers to say students should learn that a person’s gender can be different from the sex they were assigned at birth (53% vs. 5%). Most Republican teachers (69%) say students shouldn’t learn about gender identity in school.
  • Parents opting their children out of learning about these topics: 80% of Republican teachers say parents should be able to opt their children out of learning about LGBTQ issues, compared with 30% of Democratic teachers. And while 47% of Republican teachers say parents should be able to opt their children out of learning about racism and racial inequality, just 11% of Democratic teachers say this.

A majority of public K-12 teachers (58%) identify with or lean toward the Democratic Party. About a third (35%) identify with or lean toward the GOP. Americans overall are more evenly divided: 47% are Democrats or Democratic leaners, and 45% are Republicans or Republican leaners .

  • For details, refer to the Methodology section of the report. ↩

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Table of contents, ‘back to school’ means anytime from late july to after labor day, depending on where in the u.s. you live, among many u.s. children, reading for fun has become less common, federal data shows, most european students learn english in school, for u.s. teens today, summer means more schooling and less leisure time than in the past, about one-in-six u.s. teachers work second jobs – and not just in the summer, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

After home-state humbling, the question now is: What's Nikki Haley running for?

Trump's defeat of ex-governor in south carolina raises question of why she's staying in the race.

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It's become a recurring joke in recent Nikki Haley campaign speeches where she dismisses various theories about why she's still in the U.S. presidential race.

Questions about her continued participation will only escalate after her lopsided defeat in her own state of South Carolina as Donald Trump appeared headed for a roughly 20-point victory margin over Haley, the state's former governor.

This is after Haley already invited the media earlier in the week to a decidedly unusual announcement for a candidate: She's not dropping out.

Haley has lost the first four states. She even lost the race in Nevada where Trump didn't appear on the ballot, with Republicans there opting for "none of these candidates."  She's now lost in her own state of South Carolina by a wide margin, and she's trailing him by dozens of points in national polls.

Does she want to be Trump's running mate? Haley laughs off the idea in recent speeches, given how their relationship has soured . He regularly refers to her as "birdbrain."

Trump points to crowd. One person holds a sign saying, 'Haley will raise your taxes'

Is she setting up a future run? The news outlet Politico speculates as much. Or is she trying to build a brand outside politics?

"I think she's running for a board seat," Republican strategist Terry Sullivan told a podcast titled  What's Nikki Haley's Endgame?  Or perhaps, he added: "Maybe she's going to be [a TV host] on The View."

Running as a backup?

There's one scenario Haley doesn't mention in those stump speeches, and it's the one likeliest to emerge in conversation with political insiders.

It's the possibility that she's running to cement her status as a Plan B. As a backup should Trump be forced off the ticket, either by a health setback or by winding up in jail following a conviction in one of his criminal cases.

what is analysis data

Trump looks ahead to Michigan primary as Haley refuses to concede

This scenario will continue to animate conversation after her home-state defeat, as Haley promises to stay in the race at least through Super Tuesday on March 5.

Two-dozen state primaries will have happened by then, half the country. Haley said it's clear from her approximate 40-per-cent score in South Carolina that many Republicans want an alternative to Trump.

"I have a duty to give them that choice," Haley said in a buoyant speech Saturday night. "Today is not the end of our story. We're heading to Michigan tomorrow."

Talking to primary voters, it's clear that Haley supporters want her staying in. Some of these Haley supporters are Democrats. A higher-than-usual number crossing partisan lines to vote in this year's Republican primaries.

  • Analysis Nikki Haley risks home state humiliation against Trump in South Carolina
  • Trump wins Republican presidential primary in South Carolina, beating Haley in her home state

"I'm here to vote against Donald Trump," said retired Brooklyn school teacher Diane Spignardo, a Democrat who now lives in South Carolina and voted in the Republican primary outside Charleston on Saturday.

"I don't trust [Trump] with our democracy.… If he implodes, … she's a much more viable, caring person than Trump."

Andy Hagedon calls himself a libertarian who would, grudgingly, vote for Joe Biden in November to stop Trump. On Saturday, he ticked the box for Haley.

Man in baseball cap stands outside polling station, with long line in back

"I'm voting against somebody today," he said in Mount Pleasant, S.C., outside a polling station. "It's my belief that that little lying Trump doesn't deserve another four years." 

He just hoped Haley might gain a slight bump, in South Carolina and through Super Tuesday: "Enough of a bump that she can stay in.… As long as [her] campaign coffers hold out."

But it's not just Democrats.

Haley as Plan B? Could be a longshot

Republicans voiced a similar sentiment at a Haley rally, a categorical wish that Haley might linger in, if only for that Plan B scenario.

"I think she should stay in the whole way if she can because you don't ever know what's going to happen," Becky Ward Curtis said outside a rally in Georgetown, S.C., on Thursday. 

"You don't know that he's going to be on the ballot. Or [if]  something's going to happen with him."

She voted for Trump before but said she's "sick" of him and the way he treats people. She blames him for injecting a toxicity into politics unlike anything she's seen in her 77 years. 

Retiree smiling with palm trees and sunset in the background

Now comes a pre-emptive reality check.

There's no indication that even in the above scenario of an emergency replacement, the party would ever turn to Haley.

For starters, it's entirely possible — probable even — that Trump would insist on staying in, even in the event of a criminal conviction.

And some Republican observers say it's hard to imagine the party turning to Haley in the event of the other scenario: A Trump health setback.

The party would likelier turn to Florida Gov. Ron DeSantis, said Sullivan.

"Let's say Donald Trump gets hit by a meteor, … [or] choking on a Big Mac, … [do you think] everybody else will be, like, 'Oh, yeah, that's cool. Sure, no problem.… Go ahead, [Nikki Haley],'" Sullivan, who has worked on major campaigns, including in South Carolina, told the podcast. 

"She's the last person they'd pick."

Haley, son, mom in wheelchair

Depending on when the party needed an emergency replacement, it would either be decided in individual states as they name their delegates to the party's summer convention, or by the Republican National Committee.

In any case, it would provoke a frantic power-struggle.

One GOP insider-analyst said it's hard to imagine the party turning, even in a pinch, to a candidate seen as an avatar of the pre-Trump, less-nationalist GOP.

"That reflects a misunderstanding of the Republican electorate," said James Wallner, an author and political scientist at South Carolina's Clemson University, and former staffer to several high-profile Washington Republicans.

"The Republican electorate is not aligned with Nikki Haley on really important issues that are high on the public agenda right now. And given that, I find it very unlikely that they would just turn to Nikki Haley all of a sudden if they can't vote for Trump."

  • Donald Trump's 1st criminal trial confirmed for next month in New York
  • Donald Trump turns to Supreme Court in bid for presidential immunity

Haley's staunch support for Ukraine and U.S. military allies, and pro-trade stance, means she's running against the current in her party, he said.

He added she can try running for as long as donors give her money, but "it will become increasingly hard to make an argument [for donations]."

Dividing line in GOP: Not just about policy

It's a recurring theme in conversations with Trump supporters that even if their candidate failed to make it to the finish line, Haley is not their preferred alternative.

A family of four Trump supporters at a polling station in South Carolina all brushed off the idea that their former governor, Haley, might be Trump's backup.

"Nikki, it's time to get out," said Gordon Badgley. 

Group photo of elderly man and woman, he in sunglasses, she in a bright red coat, next to a middle-aged couple. In the background: a long line outside a brick school that was a polling station

His wife, Anne Badgley, said her opinion of Haley has plummeted as she campaigns against Trump: "South Carolina is done with her.… She's making a fool of herself. She's acting like she's a winner, and she's not."

Two Trump-supporting sisters said there is no Plan B.

If Trump were somehow sidelined, Deb Purcell said: "I'd handwrite him in. Everybody would." When asked about the criminal charges, she swore, calling them an outrage.

Speaking at an event where Donald Trump Jr. had just appeared in Charleston, she described Haley as a sellout to a variety of forces: Democrats, China and even financier-donor George Soros.

She distrusts most politicians. The retired financial-services worker admitted to a soft spot for one: "I do like RFK," she said, meaning third-party candidate Robert F. Kennedy Jr. — a the lawyer, environmentalist and anti-vaccine activist.

Interviews with South Carolina primary voters expose a dividing line in the Republican Party, in policy and in attitude.

Haley supporters spoke of wanting the government improved. Trump supporters of wanting the government upended, expressing deep distrust of it.

Bearded man in shirt and sport coat, Donald Trump Jr., poses for photo with woman in pink Make America Great Again cap.

Outside the polling station in Mount Pleasant, S.C., members of the Badgley clan were asked who they wanted as Trump's running mate.

Two mentioned South Dakota Gov. Kristi Noem as a possible vice-president. 

Two mentioned candidates they hope will expose the secrets of the U.S. national-security apparatus: Michael Flynn, the indicted, since-pardoned former Trump aide, and Kash Patel , whom Trump tried placing atop the CIA in the final days of his presidency and who recently threatened to prosecute journalists in a second Trump term. 

One even suggested John F. Kennedy Jr., killed in a 1999 plane crash , might still be alive and could be appointed to expose truths about his father's murder. 

Nobody mentioned their former governor, Nikki Haley.

ABOUT THE AUTHOR

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Alexander Panetta is a Washington-based correspondent for CBC News who has covered American politics and Canada-U.S. issues since 2013. He previously worked in Ottawa, Quebec City and internationally, reporting on politics, conflict, disaster and the Montreal Expos.

Related Stories

  • Nikki Haley risks home state humiliation against Trump in South Carolina

ABC News

South Carolina primary 2024: Trump projected win, Haley vows to stay in the race

F ormer President Donald Trump has won the South Carolina Republican primary, ABC News projects. It was a swift and embarrassing defeat for former U.N. Ambassador Nikki Haley, who rose to political prominence as South Carolina’s governor. Nevertheless, in her concession speech, Haley vowed to continue her campaign into Super Tuesday on March 5.

Throughout the evening, 538 reporters, analysts and contributors broke down the results as they came in with live updates, analysis and commentary. Read our full live blog below.

Latest Developments

That's a wrap.

As of 9:20 p.m., 70 percent of the expected vote is reporting in South Carolina, and Trump is leading Haley by 20 points. It’s a decisive victory for the former president, as expected, though a somewhat smaller margin than his average lead in the polls heading into today (around 28 points).

Haley’s margin of defeat, though, looks even larger when it comes to delegates: Under South Carolina’s delegate allocation system, over half the state’s 50 delegates are awarded to the statewide winner, while the rest are awarded by congressional district. It looks like the maximum delegates Haley could walk away with in her home state tonight is three, if she holds onto her lead in the 1st District.

Despite this, Haley proclaimed in her speech that tonight’s result demonstrated her home state’s frustration with the country’s direction and reiterated her promise to stay in the race.

And on that note ... We hope to see you back here for our Super Tuesday live blog on March 5! We're gearing up for an eventful night tracking not only the presidential race in 15 states (and one territory) but a slew of down-ballot primaries in Senate, House and gubernatorial races as well.

—Tia Yang, 538

Final thought: If Biden was winning only 60 percent, people would be freaking out

I have become a little obsessed tonight about what we should be expecting Trump to hit in this primary a priori . That is, given Trump is assumed to be the eventual party nominee and almost universally liked in the GOP, should he be winning more than 60 percent in South Carolina?

I already gave my case for answering "no" to that question: Strictly speaking Trump is dominating the delegate count and running ahead of his 2016 vote share in most counties with complete counts this primary cycle. And if you consider that Haley gets a home-state advantage in South Carolina tonight, Trump's adjusted vote share is close to 65 or 70 percent; our delegate benchmarks think Trump should have won 68 percent of the vote based on the demographics of the state alone. That's not the highest number, but it's not the lowest right? Would 65 percent be "good" for Trump? 75 percent? 80?

One counterargument to this centers around how the media has covered historical performances by incumbent presidential candidates. Journalist Jill Lawrence points out that in 1992, Patrick Buchanan challenged incumbent President George H.W. Bush for the GOP nomination and won 40 percent in the New Hampshire primary, holding Bush to 58 percent of the vote. That's an almost identical split to the results from tonight. The New York Times journalist Robin Toner wrote up the results with the headline "BUSH JARRED IN FIRST PRIMARY" and said the result "amounted to a roar of anger" from Republican primary voters.

If Trump was a true incumbent, I imagine the news media would use a similar headline to describe tonight's results in South Carolina. Perhaps our expectations for him are too low, or we're too focused on the broader state of play? Haley said in her concession speech tonight that she will stay in the race indefinitely, so I guess we'll get more data on Super Tuesday — only 10 days from now. The primary lives on!

—G. Elliott Morris, 538

Final thought: Looking to the suburbs

There are significant differences between primaries and general elections. (If you’re reading this live blog, I’d bet Nathaniel’s next paycheck you already know that.) But I don’t think we should lose sight of where specifically Trump is struggling in South Carolina and in the other early states: metro areas and the suburbs. Tonight, the three counties Haley won happen to be the three counties with the highest educational attainment in the state . We know that one of the primary engines of Democratic success in every cycle since 2016, really, has been improved fortunes among suburban and educated voters. Most Haley voters will end up voting for Trump, yes, but I don’t think it’s insignificant that even as he flexes control over the GOP for eight years running, his problems in the suburbs are still as evident as ever.

—Jacob Rubashkin, Inside Elections

Final thought: Haley could actually win delegates tonight

We don't have final results by congressional district (much less overall), but as we can see from a map of the results, Haley is doing better along the coast near Charleston than in much of the rest of South Carolina. That may signal that Haley could carry the 1st Congressional District once all is said and done to win three delegates. That may not seem like much, but Trump swept South Carolina's delegates in 2016, and if Haley is sticking around, winning any delegates has to be part of her strategy to carry on.

Half of the 1st District's population is in Charleston and Beaufort counties, according to Daily Kos Elections — both of which Haley currently lead in. Another 49 percent of the district lies in Berkeley and Dorchester counties, both of which Trump holds an edge in (the remaining 1 percent is in Colleton and Jasper counties). Charleston and Dorchester are both split between the 1st and 6th, so we can't figure out the district-level result based purely on the county-level numbers. So we'll have to see. But Haley's showing in the 1st might be her one bright spot tonight.

—Geoffrey Skelley, 538

Final thought: The primary is over, long live the primaries

You’re right, Nathaniel, this isn’t a real primary. If nothing really dramatic happens, Trump is going to easily walk to the nomination. My silver lining: Super Tuesday marks the start of our downballot primary season, with primary races for Senate in California and Texas, and primaries for House races in five states. So perhaps we’ll have something more competitive to talk about soon.

—Mary Radcliffe, 538

Final thought: Haley is a unique candidate

Well, this wasn’t the most eventful primary night I’ve ever live-blogged. But this gave me a chance to reflect on the increasingly unusual nature of Haley’s candidacy. I can see an emerging narrative about her speech, in which she positioned herself directly against the polarization represented by a Biden-Trump matchup. Haley is a former member of the Trump administration, yet she’s positioned herself more and more as a Trump alternative — not a Trump substitute. She’s focused mainly on the electability issue, but she did sound a bit like a center-right third party candidate there. That’s not all that struck me though — Haley has also run an explicitly gendered campaign, talking about her 5-inch heels and “if you want something done, ask a woman.” She is only the second woman to win delegates in Republican primaries or caucuses. (Carly Fiorina was the first.) As we track the (Trump-dominated) horse race, we shouldn’t lose sight of what a unique candidate she is.

—Julia Azari, 538 contributor

Final thought: A woman of her word …

If Haley truly does drag this out through Super Tuesday, I’m curious what she expects to gain from losing in a couple dozen more states. I get that her motivations are bigger than becoming the nominee at this point, but will such a thorough thumping serve such goals? Only time will tell!

— Kaleigh Rogers, 538

Final thought: This is not a real primary

Tonight, Trump became the first non-incumbent Republican in the modern primary era to win all three of Iowa, New Hampshire and South Carolina. Haley has failed to win New Hampshire despite demographics that were practically engineered in a lab to be good for her, and she failed to win South Carolina despite it being her home state. Trump is going to be the nominee; it’s time to start treating the primary as over.

—Nathaniel Rakich, 538

Final thought: I agree she sounds like a third-party candidate

Jacob, I agree that Haley sounded a bit like a third-party candidate tonight, taking on both frontrunners. If any voters were hoping to avoid a Trump vs. Biden rematch, it seems increasingly clear that they won't get their wish. Haley is staying in and attacking both Biden and Trump as two versions of the same thing: old, out of touch and not ready to take the country into the future. She still hasn't fully attacked Trump for his role in Jan. 6 and the court cases against him, which might win her some points with the non-Republican electorate, and it hasn't worked for her so far to try to make the age case against him. I don't think any of this will work, but we'll also have to see how Super Tuesday unfolds, since she seems to be staying in.

—Monica Potts, 538

South Carolina primary 2024: Trump projected win, Haley vows to stay in the race

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